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The latest AI stories, analysis and developments relevant to Healthcare — curated daily by Best Practice AI.
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200 articles
Ethics and EU AI Act in Cases of Work Disability Risk and Alzheimer's Disease Risk Prediction
arXiv:2607.05402v1 Announce Type: new Abstract: Improvements in AI technologies have made it feasible to develop new types of medical AI tools. However, these tools raise new kinds of questions, especially in relation to the ethics and AI Act compliance. We analyzed two cases of AI tools developed to predict medical risks, the risk of work disability (case A) and the risk of getting Alzheimer's disease (case B). We observed both cases using the ethical AI and the EU AI Act as frameworks, noted that they classify as high-risk systems, and that bringing them from the research environment to production would require a lot of work and compliance due to the related regulation.
Most nurses say AI isn’t good enough to trust with patient care, survey finds - The Washington Post
About 44 percent of nurses use AI at work, up from 15 percent last year, according to a survey of over 2,200 nurses released Tuesday.
AI-supported mammography catches more cancers without raising false positives
A multicenter study in Nature Cancer found AI-supported mammography caught more clinically relevant cancers without raising the false-positive rate.
From Adoption to Governance in Radiology: Navigating the Next Wave of Imaging AI | Diagnostic Imaging
In order to fully take advantage of the capabilities of AI, this author emphasizes key principles and a framework for converting isolated technology solutions into reliable components of radiology workflows and operations.
World Feedback for Clinical Agents: Diagnosing RL in FHIR Environments
arXiv:2607.01470v1 Announce Type: new Abstract: Clinical protocol-execution tasks -- checking a lab value, applying a threshold, placing a correctly structured FHIR order -- are natural candidates for RL from world feedback: once clinical SMEs encode decision logic into a verifier, that verifier grades unlimited rollouts without per-episode annotation. But applying RL requires a sound feedback channel and sufficient base capability. We audit MedAgentBench v1/v2, find a 41.7\% silent-finish ceiling that makes inaction the RL dominant strategy, and construct \textbf{MedAgentBench-v3 (MAB-v3)} (508 tasks, 8.9\% ceiling). Training Qwen3-8B exposes two structural barriers: a \emph{capability ceiling} (10/20 task types have 0\% base performance, zero gradient) and a \emph{format-knowledge barrier} (3/20 types require exact clinical codes undiscoverable by exploration). Pure RL reaches 18.2\% pass@1 vs.\ 34.1\% for rule-based SFT; the 15.9~pp gap is attributable entirely to these barriers. A decision/format-knowledge/lookup taxonomy predicts RL learnability and prescribes the fix: SFT to inject codes, RL to learn conditionals.
Three Futures for the Diagnostic Radiologist: A Structured Disagreement About What AI Actually Changes
arXiv:2607.01253v1 Announce Type: new Abstract: Rationale. The diagnostic radiologist's role in 2035 will not look like it does today. Imaging AI is already changing how worklists are organized, how reports are generated, and which cases require a radiologist's attention. What remains genuinely contested is not whether the role changes but how. Approach. Three subject-matter experts (two radiologists and one health tech professional with more than 20 years of experience in medical imaging IT) independently authored 2035 job descriptions for the diagnostic radiologist using a shared template. Each author wrote from a distinct vantage point: one optimistic, one framed as a trade-off view incorporating workforce economics, and one structured around professional stratification. The three versions were published openly and subjected to a structured comparison across seven dimensions. Key findings. The three versions agree on direction but disagree on magnitude. All three describe a radiologist whose routine workload is AI-managed, who carries accountability for AI output, and who spends more time on complex cases and clinical collaboration than today's radiologist does. They diverge on headcount, career security, and whether the profession expands broadly, concentrates into a smaller well-compensated group, or stratifies into sharply differentiated tiers. Conclusion. AI won't eliminate the diagnostic radiologist. Whether it expands, concentrates, or stratifies the profession depends on choices health systems haven't made yet. The clinical argument for optimism is real. So is the economic argument for caution. Both can be true simultaneously. Keywords: radiology workforce; artificial intelligence; diagnostic radiology; job redesign; medical imaging IT; AI governance
Discrete Diffusion Language Models for Interactive Radiology Report Drafting
arXiv:2607.01436v1 Announce Type: new Abstract: Diffusion language models, which generate text by denoising a token canvas bidirectionally instead of emitting tokens left to right, have become competitive with autoregressive (AR) generation. Medical foundation models, however, remain almost entirely autoregressive. We adapt a mixture-of-experts diffusion language model, DiffusionGemma-26B, and benchmark it against its same-size AR sibling Gemma-4-26B under an identical LoRA recipe on medical visual question answering datasets, scored by a verbosity-robust LLM judge. Diffusion matches or exceeds AR on all of them, and the finetuned model (3.8B active) is competitive with frontier vision-language models; its decoding is also 3.5-4.4x faster. Beyond this parity, the diffusion model offers a drafting capability AR lacks: any-order infill. Because the canvas is denoised bidirectionally, a radiologist can fix report fragments and have the model fill the text between them, an operation inherent to diffusion but not to autoregression, which is subpar at it. This suits real reports, which are often terse or inconsistent across clinicians and institutions.
RareDxR1: Autonomous Medical Reasoning for Rare Disease Diagnosis Beyond Human Annotation
arXiv:2607.00147v1 Announce Type: new Abstract: Rare disease differential diagnosis is a critical yet arduous clinical task, requiring physicians to identify precise phenotypes from complex, unstructured patient symptoms and execute intricate reasoning within a vast search space. However, existing AI approaches typically rely on pipeline-based phenotype extraction or retrieval-augmented generation, which suffer from critical information loss due to predefined ontologies, retrieval bottlenecks, and a lack of diagnostic logic. To address these challenges, we introduce RareDxR1, an end-to-end reasoning-centric large language model designed for open-domain rare disease diagnosis directly from unstructured clinical notes. We design a progressive end-to-end training framework by synergizing knowledge internalization with autonomous evolutionary learning, thereby bypassing reliance on structured phenotypes and closed-set decision-making. To overcome the limitations of RAG and phenotype restriction, we enabled the deep internalization of fragmented rare-disease knowledge directly into the model's parameters. Moreover, to bridge the gap between model generation and expert reasoning, we propose Reflection-Enhanced Reasoning Sampling (RERS), a strategy that synthesizes expert-level diagnostic trajectories by learning from failures without human annotation. Additionally, we propose a dual-level curriculum reinforcement learning approach for gradually mastering rare disease diagnosis. Experimental results demonstrate that RareDxR1 achieves state-of-the-art accuracy across different benchmarks, marking a significant breakthrough in open-domain rare disease diagnosis. Our code and dataset will be publicly available.
Can Physician Expertise Improve Machine Learning Identification of Delirium?
arXiv:2606.30651v1 Announce Type: new Abstract: Delirium is common in hospitalized patients and is often missed in routine care. We present a user-centered interactive machine learning (UC-iML) framework for delirium detection support that combines physician-guided feature refinement with interpretable modeling. Using 3,862 labeled admissions from six Toronto hospitals in the General Medicine Inpatient Initiative (GEMINI), we integrate administrative variables, laboratory results, medications, and a radiology-derived text indicator. Physicians guide feature refinement and model evaluation, and Shapley Additive exPlanations (SHAP) are used to summarize feature attribution. We evaluate standard supervised classifiers with temporally separated holdout testing and a later-phase validation cohort. Compared with automated and baseline variants, the proposed framework shows better overall discrimination and stronger temporal robustness, while the explanations highlight clinically meaningful signals. These results support UC-iML as a practical human-in-the-loop framework for clinically relevant delirium modeling.
Agentic AI Enhances Physician Trust in Clinical Decision Making
arXiv:2606.30658v1 Announce Type: new Abstract: Medical AI has shifted from reasoning to agentic AI, a new paradigm that autonomously invokes external tools during reasoning, rendering intermediate reasoning steps and tool outputs transparent to users. Although proven to outperform previous models, physician trust in agentic AI remains largely unexplored. To address this, three physicians evaluated 315 multimodal clinical cases quantifying both process-oriented cognitive trust and outcome-oriented behavioral reliance. Comparing agentic AI against non-agentic baselines, physicians exhibited significantly higher cognitive and behavioral trust for the agentic model (P < 0.001). Specifically, on treatment planning tasks, physicians trusted the agentic reasoning most, preferring it in 89.57% of cases. Furthermore, process-oriented cognitive trust is significantly associated with outcome-oriented behavioral reliance (P < 0.001). However, measurable over-reliance on incorrect agentic outputs still exists, highlighting the inherent limitations of decision-logic transparency alone and underscoring the continuous need for rigorous clinician oversight.
AI for Quality Assurance in the Operating Room
arXiv:2606.30657v1 Announce Type: new Abstract: Surgical outcomes depend not only on patient factors and postoperative care but are also strongly influenced by the quality of the operation itself. Yet, for much of mod-ern surgery, intraoperative quality has been assessed indirectly through outcomes and operative reports. The increase in minimally invasive procedures inherently guided by endoscopic video, together with advances in artificial intelligence, creates an unprecedented opportunity to systematically observe, measure, and improve surgi-cal care. This chapter introduces AI-enabled Surgical Quality Assurance as a frame-work for using surgical data to support continuous assessment and improvement in the operating room. We first review existing approaches to surgical safety, from sys-tem-level interventions to procedure-specific standards. We then describe how AI can transform intraoperative video into clinically meaningful information, including recog-nition of anatomy, instruments, workflow, surgical actions, quality criteria, adverse events, and critical moments. Finally, we outline the major challenges that must be addressed before these systems can deliver routine clinical value, including representa-tive data collection, robust validation, workflow integration, regulation, liability, pri-vacy, and equitable access. Rather than replacing surgical judgment, AI for quality assurance should be understood as a set of tools for augmenting the surgical team, scaling expert review, and helping surgery evolve toward a learning system in which intraoperative care is continuously observed, assessed, and improved.
Explainable Artificial Intelligence For The Detection and Characterisation of Stage B Heart Failure
arXiv:2606.30665v1 Announce Type: new Abstract: Stage B heart failure is characterized by asymptomatic structural or functional cardiac abnormalities. Identifying individuals at this stage is clinically important, as early detection may enable targeted interventions to prevent progression to symptomatic disease. Explainable artificial intelligence (XAI) may support early detection, transparent risk stratification, and selection of clinically actionable interventions. This review examines the use of XAI in detecting and characterizing stage B heart failure. A literature search of Web of Science, Scopus, and PubMed was conducted on 27 March 2026. Studies were included if they applied AI with XAI techniques to stage B heart failure. After screening, 20 studies were included. Data on modalities, outcomes, demographic reporting, and XAI methods were extracted and synthesized. SHAP was the most commonly used method, followed by LIME, saliency maps, and Grad-CAM; however, XAI adoption was inconsistent, with some studies relying on limited or ad hoc interpretability approaches. Notably, none compared explanations across sex or ethnic subgroups, despite evidence of subgroup differences in disease burden. Evaluation of XAI outputs was often insufficient: some studies did not assess explanations, while others relied only on literature-based comparisons, introducing potential bias. These limitations suggest explainability was not systematically validated or leveraged to support robust and fair clinical inference. XAI shows promise for improving transparency in stage B heart failure identification, but current implementations remain limited. Key gaps include limited consideration of sex and ethnicity, absence of subgroup-specific analyses, inconsistent evaluation, and lack of external validation, all of which constrain generalisability and clinical adoption.
Frequent AI chatbot users more likely to believe anti-vaccine myths, poll finds
Poll finds use of AI tools for health advice is correlated with belief in vaccine falsehoods, such as shots causing autism Adults in the US who frequently seek out health advice from artificial intelligence chatbots are more likely to believe myths about vaccines, according to a poll released on Tuesday by health research firm KFF. The survey, which was conducted in May and polled a representative sample of 2,480 US adults, found that use of AI tools and chatbots correlated with belief in falsehoods such as vaccines causing autism or that the measles vaccine poses more danger than the corresponding virus. The connection remained while controlling for factors such as age, race, education and political partisanship. Continue reading...
IMCBench: A benchmark for multimodal LLMs in Image-grounded Medical Conversations
arXiv:2606.28556v1 Announce Type: new Abstract: Recent advances in large language models and vision-language models have enabled reasoning over multimodal data, offering opportunities for clinical applications such as decision support and triaging. However, existing medical AI benchmarks are fragmented: some support multi-turn dialogues but lack images, while others provide multimodal inputs but focus on single-turn QA tasks. To address this gap, we introduce IMCBench, an image-grounded, multi-turn medical conversation benchmark that pairs real, publicly available clinical images with synthetic patient profiles to simulate realistic patient-clinician interactions. Each conversation is evaluated across three clinical dimensions: safety, accuracy, and appropriate use of uncertainty in diagnosis. We benchmark eight multimodal frontier models across four model families (Claude, GPT, Nova, and Llama), scoring each on a 1-5 scale using LLM-as-Jury scoring calibrated against expert clinician annotations. Our results show that Claude Opus 4.6 achieves the highest overall score (3.61), followed by Claude Sonnet 4.6 (3.30) and GPT-5.2 (3.29), though no model dominates all dimensions and safety degrades for both malignant and rare conditions ($\Delta$ = -0.27 each). Ablation studies further reveal that both visual input and EHR context contribute to safe guidance (safety drops of 0.18 and 0.23 on average when each is removed), with stronger models leveraging visual features more effectively. Together, these findings demonstrate that accurate clinical description does not guarantee safe patient guidance, motivating the need for multi-dimensional evaluation frameworks in medical AI.
Lawmakers want to ban AI companies from selling your health data
New legislative efforts are underway to prevent AI companies from monetizing sensitive health and location data.
When Medical Safety Alignment Fails: A Benchmark for Evaluating LLMs on High-Risk Medical Queries
arXiv:2606.28332v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for medical and health-related questions, yet their safety in high-risk medical scenarios remains poorly understood. We introduce \textsc{MedHarm}\footnote{Code and data will be released upon acceptance. Due to the sensitive nature of high-risk medical queries, data access will be available to qualified researchers upon request.}, a high-risk medical safety benchmark with 1,100 medically grounded queries across 10 safety-critical categories, including toxicology, pharmacology, covert poisoning, anesthesia, and fetal harm. Unlike broad medical QA benchmarks, \textsc{MedHarm} targets realistic clinical, educational, and technical prompts that require refusal, caution, or safe redirection rather than direct helpfulness. We evaluate 15 LLMs spanning general-purpose, medical-purpose, closed-source, and downstream SFT models, together with 4 representative guardrail models. Results reveal a substantial gap between apparent alignment and medical safety: aligned models can still produce unsafe or actionable responses, medical fine-tuning can amplify harmful specificity, and external guardrails reduce some failures while introducing brittle blocking and weak safe helpfulness. These findings show that medical safety cannot be inferred from general alignment or medical capability alone, highlighting the need for domain-specific stress testing before deploying LLMs in safety-critical medical applications.
Use of artificial intelligence for mental health splits opinion
Two-thirds of those aged 25 to 34 have asked chatbots for wellbeing support
Ground Truths in Suicide Research: The Current State of AI-Based Suicide Detection in Social Media
arXiv:2606.28334v1 Announce Type: new Abstract: Recent advances in artificial intelligence (AI) and social media data have led to growing optimism about the ability to detect suicide risk at scale. However, the empirical foundations of this work remain unclear. This article provides a synthesis of current research on AI-based suicide detection in social media, drawing on a recent umbrella review of 22 systematic reviews covering studies up to 2022, alongside an ongoing literature review extending the analysis to more recent work. Across these sources, we identified 195 relevant studies, which are documented in a detailed supplementary dataset outlining their key characteristics and findings (see Supplementary Information). Analysis of these studies reveals consistent patterns, including rapid growth, concentration on a small number of platforms, reliance on textual and English-language data, and repeated use of similar datasets. Most importantly, the majority of studies rely on indirect labeling strategies that do not involve direct, individual-level validation of suicide risk. Instead, ground truth is typically inferred from observable features of online content, such as linguistic markers or community membership. As a result, the predictive task often shifts from identifying individuals at risk to classifying posts that contain suicidal or distress-related language, limiting the ability of current approaches to detect individuals who do not express such content explicitly online. These findings suggest that current advances in model performance should be interpreted with caution. Progress in this field is likely to depend less on improving model performance and more on ensuring that model predictions meaningfully correspond to suicide risk as it is experienced in real life.
PySynthea: A Python-Native Framework for Scalable Synthetic Healthcare Data Generation
arXiv:2606.28346v1 Announce Type: new Abstract: Synthetic healthcare data is increasingly important for research, education, and machine learning development where access to real patient data is limited by privacy and governance constraints. While Synthea provides a widely adopted framework for generating realistic longitudinal electronic health record data, its current implementation presents adoption barriers for many researchers and data scientists due to deployment complexity and limited integration with modern Python-based workflows. This paper introduces PySynthea, a Python-native reimplementation of Synthea designed to improve accessibility, extensibility, and interoperability within the scientific Python ecosystem. The framework provides modular synthetic patient generation, configurable healthcare simulation pipelines, and support for standard healthcare data formats while integrating naturally with tools such as pandas and machine learning workflows. By reducing operational complexity and aligning synthetic data generation with the dominant data science ecosystem, PySynthea aims to accelerate experimentation and broaden the use of synthetic healthcare data in research and applied AI development. The code in this github repository https://github.com/TIET-AI/tietai-synthea.
I used Claude Code to get a second opinion on my MRI
An exploration of using AI tools like Claude Code to assist in the analysis of medical imaging.
Cork medtech NeuroBell bags $5.5m for its AI neonatal monitor
NeuroBell hopes to nab US clearance for its AI-powered neonatal monitor Luna this year. Read more: Cork medtech NeuroBell bags $5.5m for its AI neonatal monitor
Benchmark Scores Don’t Break. Clinical Reality Does. The Health AI Readiness Illusion.
Nature Medicine's frontier AI evaluation exposes a dangerous gap: models acing medical benchmarks collapse under adversarial pressure, and the...
‘Cop on your wrist’: Wearables offer tons of data, but people are still going to sleep to Netflix and TikTok
A group of experts at the Fortune Brainstorm Tech event in Aspen pinpointed a problem: People have more data than ever from their wearables, but nobody knows what to do with the information.
AI Revolutionizes Healthcare: Enhancing Patient Care and Hospital Efficiency with Smart Technology
AI is revolutionizing healthcare by enhancing disease detection, treatment planning, and operational efficiency, ultimately improving patient care.
Knowledge-augmented Agentic AI for Mental Health Medication Information Seeking
arXiv:2606.26205v1 Announce Type: new Abstract: Patients increasingly seek medication information online, yet safety knowledge for psychiatric drugs is split between regulatory adverse-event records, which are authoritative but abstract, and patient narratives, which are experience-near but unvalidated. Integrating them without conflating evidence and anecdote is especially consequential in psychiatry, where poorly contextualised information can amplify fear, nocebo responses, and non-adherence. Here we develop a provenance-aware, knowledge-graph-based multi-agent framework unifying 466,525 Reddit posts, 60,782 WebMD reviews, and twenty years of U.S. FDA Adverse Event Reporting System records for nine antidepressants. A large-language-model entity-recognition pipeline benchmarked against physician annotations reached highest F1 scores of 0.969 for medications and 0.973 for conditions. The two community platforms were far more concordant with each other (overlap up to a Jaccard similarity of 0.905) than with regulatory reports, indicating that patient-generated data form a partly independent safety signal. For sertraline, many adverse events appeared in community sources hundreds of days before the corresponding FDA date. A Neo4j knowledge graph grounded in ATC-N, ICD-10, and MedDRA vocabularies preserves provenance, keeping every claim traceable and regulatory facts distinct from patient experience. These results establish source-aware integration as a route to more auditable psychiatric medication information, with usefulness and patient benefit to be tested prospectively.
Johns Hopkins already leads the medical world. On AI, it won’t be easy. - The Banner
Johns Hopkins is investing heavily in AI research and medicine, including training a robot to help in the operating room.
Is there a business case for AI tools in a small practice? | Medical Economics
The number of doctors using AI tools is rapidly increasing, but for a smaller practice, does the business case make sense?
Evaluating the robustness and readiness of large frontier models in health AI applications | Nature Medicine
Large frontier models such as GPT-5 and Gemini have demonstrated remarkable performance in a wide range of health application benchmarks. However, underneath the seemingly promising results lie salient growth areas, especially in cutting-edge frontiers such as multimodal reasoning.
Simulating Eating Disorder Patients with LLMs: Evaluating Psychological Persona Stability in Multi-Turn Conversations
arXiv:2606.26109v1 Announce Type: new Abstract: Large language model (LLM)-based simulations of clinical patients are increasingly used for research and training, yet their validity requires persona stability: coherent maintenance of an assigned psychological profile across and within conversations. We evaluate this prerequisite using eating disorder personas grounded in five published case vignettes, a dual-assessment framework (self-report + independent observer ratings), and validated psychometric instruments (EDE-Q) with known ground-truth scores. Across six LLMs and two experiments (between-conversation stability (Exp. I) and within-conversation stability (Exp. II)), we find that LLMs are paradoxically too stable and too inaccurate: variability is negligible, yet all models systematically overshoot ground-truth severity by 12-30% of the scale range (0.7-1.8 points on a 0-6 scale). The mechanism is selective stereotyping: models differentiate cases on behavioural items (dietary restraint) but maximise cognitive-affective items (body dissatisfaction, weight preoccupation) at ceiling regardless of case severity. Additional conversational context does not improve accuracy; it compounds the overshoot. LLMs can portray severe eating pathology but lack a representation of moderate clinical presentations, a "missing middle".
If the FDA isn’t regulating every aspect of AI in healthcare who will? Executives debate at San Diego waterfront
An FDA director spoke with pharma executives, AI engineers, lobbyists, politicians, and entrepreneurs from around the world at BIO International to answer one question.
AI Is Everywhere in Medicine. Why Aren’t Students Being Trained to Use It Responsibly?
As new clinical AI tools proliferate, students are growing worried about the burden of keeping up. Are medical schools doing enough to train them?
T2D-Bench: Evidence-Gated Evaluation of LLM Outputs for Type 2 Diabetes Using a Multi-Layer Clinical-Lifestyle Knowledge Graph
arXiv:2606.24145v1 Announce Type: new Abstract: Large language models (LLMs) can produce clinically fluent recommendations for type 2 diabetes while failing to satisfy guideline constraints or explicitly justify lifestyle-related glycemic claims. We present T2D-Bench, a reproducible benchmark and evidence-gated evaluation framework for testing whether LLM outputs satisfy explicit, graph-checkable evidence requirements. T2D-Bench is built on a multi-layer clinical-lifestyle knowledge graph that combines a biomedical spine (UMLS, DrugBank, SIDER), computable ADA Standards of Care rules, and lifestyle knowledge connected through a mechanistic bridge to glycemic laboratory effects. Across 100 structured vignettes spanning diagnosis, medication safety, and adversarial lifestyle conflicts, baseline outputs failed benchmark-defined evidence-path checks in 35% of cases for GPT-4o-mini and 33% for GPT-4o. The evidence gate detects unsupported omissions and uses constrained revision to bring outputs into verifier-level compliance with benchmark-defined evidence requirements. These results show that computable evidence constraints can make unsupported clinical omissions explicit, measurable, and correctable in diabetes-focused LLM outputs.
Ensemble Feature Selection and Harris Hawks Optimization for Explainable Mental Health Risk Prediction in Female Sex Workers
arXiv:2606.24047v1 Announce Type: new Abstract: One of the significant mental health issues affecting female sex workers (FSWs) is mental disorders, especially depression. Exposure to violence, stigma, and economic hardship further increases their psychological risk. Current machine learning (ML) models are typically ineffective at capturing the high-dimensional and complex risk patterns that exist in this marginalized group. This paper suggests a hybrid predictive model that merges an ensemble feature selection strategy using ANOVA and mutual information and Harris Hawks optimization-tuned logistic regression and represents a new application of swarm intelligence to predict mental health in vulnerable groups. The explainable AI (XAI) methods can be used to understand the factors of trauma associated with model predictions. When applied to a group of 3,005 FSWs, it can be seen that the proposed model is more effective than traditional classifiers, with an accuracy of 95.78%, an F1 score of 95.77%, and an AUC of 0.96, and identifying post-traumatic stress, client-related violence, and occupational factors as major contributors to depression. This work bridges the gaps between conventional and ML approaches to develop an XAI tool that enables vulnerable groups to receive early assistance, evidence-based targeted psychosocial care, and health planning.
Medical diagnosis AIs can be tricked into telling whose data trained them
Did you read all the documents you signed last time you had a medical test?
When AI Makes the Call, Doctors May Take the Blame
For all its promise and computational power, will AI in clinical medicine overcome physicians’ concerns?
Nvidia’s Kimberly Powell: We are reinventing the doctor experience
The chipmaker’s head of healthcare argues AI can ease many of the sector’s ills, including reducing medics’ workload and tackling the shortage of trained staff
1 in 3 Americans use chatbots for health advice. These 6 patients explain why. - The Washington Post
They say cost, speed and other considerations drove them to AI . They are also learning what to believe and what to take with caution.
AI in Medical Imaging Market to Reach $8.56 Billion by 2030 at 30% CAGR | GE Healthcare, Butterfly Network, Enlitic Among Key Players
The global AI in medical imaging market valued at 1 75 billion in 2024 will reach 8 56 billion by 2030 expanding at a compound annual growth rate CAGR of 30 over the forecast period The scale of that growth ...
GEM Hospital Unveils India's First Dual-City Robotic Surgery Network
GEM Hospital launched OPERATION INFINITY, a dual-city robotic surgery network connecting Chennai and Coimbatore for real-time collaborative procedures.
AI-Driven Deskilling in Healthcare and Elsewhere
As more professionals become overly reliant on AI tools, AI-driven deskilling is about to become widespread across every occupation. We are not talking about it enough | Edition #300
Agentic AI Comes to Medicine - by Eric Topol
This was designed to be embedded in a health system EHR to provide reasoning and action steps. There were 2 agents, the patient and the AI physician (MIRA). MIRA queried the patient’s history, the physical exam results, and could order labs, blood cultures, scans, medications, procedures, surgery, and triage for hospital admission.
OpenAI's GPT-5.5 Instant Revolutionizes Health Guidance, Cuts Medical Misinformation by 71%
OpenAI introduces GPT-5.5 Instant, claiming enhanced accuracy in health guidance, reducing medical misinformation by 71% over two months.
AI Breakthrough: OpenAI, Boston Children's, and Harvard Uncover 18 New Pediatric Diagnoses
OpenAI's o3 Deep Research team has teamed up with Boston Children's Hospital and Harvard to identify 18 new pediatric diagnoses from 376 previously unsolved cases.
LLM Doesn't Know What It Doesn't Know: Detecting Epistemic Blind Spots via Cross-Model Attribution Divergence on Clinical Tabular Data
arXiv:2606.19509v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly applied to structured clinical data, yet whether they can recognize the limits of their own knowledge on such tasks remains unexplored. We study this question through the lens of cross-model attribution divergence with the goal of reducing epistemic uncertainty for structured tasks, comparing Qwen 2.5 7B and XGBoost on a prediction task via attribution divergence analysis. We report four findings. First, LLM verbalized confidence is epistemically vacuous, it outputs a near-constant (0.856-0.937) regardless of whether accuracy is 49% or 75.3%, tracking prompt format rather than prediction quality. Second, the LLM exhibits an inverse difficulty effect: accuracy drops to 64.8% when XGBoost is 99% correct, but matches XGBoost (73.8% vs. 73.1%) when it is moderately uncertain. Third, few-shot examples and SHAP-derived feature evidence are orthogonal, super-additive interventions: they reduce the Attribution Disagreement Score (ADS) from 1.54 to 0.38 and improve accuracy from 49% to 75.3% without training. Fourth, a cross-model calibrator that determined LLM reliability using attribution divergence signals reduces expected calibration error from 0.254 to 0.080, replacing uninformative verbalized confidence with patient-specific reliability estimates, without accessing model internals or requiring repeated inference. We frame these findings as a cold start problem for LLMs on structured data and outline a path toward genuine epistemic self-awareness.
REVEAL++: Differentiable Phenotypic Grouping for Vision-Language Retinal Modeling of Alzheimer's Disease Risk
arXiv:2606.19522v1 Announce Type: new Abstract: The retina offers a noninvasive window into neurodegenerative disease, capturing subtle structural patterns associated with a risk of future cognitive decline. Vision-language alignment frameworks such as REVEAL have shown that pairing retinal fundus images with structured clinical risk narratives improves early prediction of Alzheimer's disease (AD). A key design choice in these approaches is the use of phenotypic grouping, where individuals with similar risk profiles are treated as multi-positive pairs during contrastive learning. However, existing methods operationalize phenotypic similarity as a discrete construct, relying on hard group assignments that impose rigid supervision and decouple group formation from representation learning. We propose a continuous formulation of phenotypic structure within contrastive learning. Rather than assigning samples to fixed clusters, we model inter-subject similarity as a differentiable weighting function derived from intra-modality embedding similarities in both retinal images and risk profiles. These weights define soft multi-positive relationships through a continuous aggregation operator, enabling graded supervision that reflects the spectrum nature of disease risk. We further introduce a soft-target contrastive objective that jointly learns cross-modal alignment and phenotypic structure in an end-to-end manner. Evaluated on UK Biobank retinal imaging data for incident AD prediction, the proposed framework consistently outperforms discrete group-based contrastive learning and standard vision-language baselines. By treating phenotypic similarity as a learnable, continuous signal rather than a fixed grouping rule, our approach provides a principled and robust foundation for population-scale neurodegenerative risk modeling from multi-modal retinal and clinical data.
Configurable Clinical Information Extraction with Agentic RAG: What Works, What Breaks, and Why
arXiv:2606.19602v1 Announce Type: new Abstract: Patient contexts span hundreds of heterogeneous documents and thousands of structured data points, yet the document-level metadata that AI systems need for retrieval and triage is absent or incomplete. Standard retrieval-augmented generation fails on this data, mishandling temporal reasoning, cross-document dependencies, and missing metadata. We dep
Midjourney pivots from AI image generation to body scanning medical spa where patients bathe in 'golden light'
Midjourney is reportedly shifting focus toward a medical spa concept, utilizing technology borrowed from an undisclosed partner.
Brain-computer interface trials are taking off
This week, I covered the story of Casey Harrell—a man with ALS who is “the first power user” of a brain implant, according to the researchers who worked with him. Harrell is paralyzed and unable to speak coherently without the device. He has now spent almost three years using a brain-computer interface (BCI) that enables…
UnitedHealth’s $3 Billion AI Push Has Bots Calling Doctors
At UnitedHealth Group Inc., artificial intelligence reads aloud summaries of medical charts as nurses drive to patients’ homes. It listens to millions of customer calls to find the causes of complaints. One trial even has AI agents calling doctors’ offices to schedule appointments for patients.
AI Startup Midjourney Pivots to Health With Ultrasound Machine
AI startup Midjourney Inc. announced its first hardware project at an event in San Francisco, outlining an unexpected move into the personal health and medical industries.
Midjourney Medical goes from generating ‘cat images’ to full-body ultrasound scans
Midjourney's AI technology is being applied to medical imaging, specifically for ultrasound scans.
RELIANCE: Curating and Evaluating Reproductive Health Information on Social Media
arXiv:2606.18285v1 Announce Type: cross Abstract: Social media platforms like TikTok have become a key source of health information, with studies reporting inaccuracies in posts. As Large Language Model (LLM) providers increasingly integrate LLMs into digital platforms to fact-check content (e.g., Grok and Perplexity on X and WhatsApp, respectively) and are being used by people to fact-check information, deploying these systems in critical areas such as reproductive health without rigorous evaluation can cause serious harm. We introduce RELIANCE, an expert-annotated dataset of health information on TikTok surrounding pregnancy and postpartum queries, serving as both an analysis of the reproductive health information landscape and an evaluation of LLMs' capabilities in fact-checking this content. Our dataset comprises 409 annotated sentences from 336 videos across 56 clinician-reviewed queries, annotated by three expert clinicians in Obstetrics, Gynecology, and Internal Medicine. Our findings reveal that nearly 60\% of the health information in the videos we sampled is accurate. Furthermore, LLM evaluations reveal a gap between evaluating specific claims and evaluating the entire content (15\%). We believe that our methodology, dataset, and tool will support the machine learning community in improving LLMs for important domains with real-world data, extending to other platforms and languages, and helping the health community further understand the information landscape on social media. Our dataset and code are made available at https://realize-lab.github.io/RELIANCE/.
"Are you an AI?" Analyzing Client Suspicion of AI Use in Crisis Counseling
arXiv:2606.18261v1 Announce Type: cross Abstract: As artificial intelligence (AI) tools get increasingly deployed for mental healthcare, public trust in these systems remains uncertain. It is unclear how clients perceive AI involvement in counseling interactions, particularly in moments of crisis that require empathy and connection. To address this gap, we analyzed 75,777 crisis counseling conversations from a human-staffed WhatsApp helpline in India to characterize how often clients suspected they were speaking to AI, what triggered those doubts, and how counselors responded. Though no conversations actually involved AI assistance, the proportion of conversations where clients suspected AI use increased from 0.8% in June 2024 to 2.6% in March 2025. Within suspicious conversations, 21.5% of clients stated an explicit preference for humans. Client suspicion primarily arose in the first half of messages (68.3%), and when counselors offered reassurance (e.g. 'I assure you; this is not ai!'), clients continued to press or ended the conversation 17.6% of the time. As AI tools get increasingly integrated into counselor workflows, understanding these dynamics is essential for designing AI systems that preserve the therapeutic relationship between counselors and clients.
Strategic Feature Selection
arXiv:2606.18867v1 Announce Type: cross Abstract: When algorithmic predictors inform resource allocation in high-stakes domains such as healthcare, these predictors must account for strategic manipulation of input features. The typical solution is to redesign the predictor itself to explicitly account for strategic interactions. In practice, however, decision makers are often constrained to adjusting coarser levers within existing prediction pipelines. For example, healthcare organizations often select which features to exclude based on perceived manipulability, while using standard regularization procedures to shrink the coefficients of retained features. In this work, we initiate a formal study of strategic classification through feature selection and its interaction with ridge regularization. Our main finding is that excluding individual features based on their manipulability alone is generally suboptimal. We provide a fine-grained characterization of the performance of a feature subset under optimal regularization, yielding new insights for policy design. Motivated by this characterization, we develop a practical algorithm for jointly choosing the feature set and the level of ridge regularization. Through a real-world case study on a healthcare payments benchmark, we illustrate how our algorithm can guide the design of coarse policy levers in practice. Our results provide a principled, practical framework for mitigating the effects of strategic behavior in algorithmic decision-making systems.
AI Health Startup Wants to Assist Half of Latin American Doctors
An Andreessen Horowitz-backed healthcare startup born in Latin America wants to put its AI assistant in the hands of half the region’s 1.9 million doctors by the end of 2027, a bet that technology can help bridge a shortage of medical professionals across strained health systems.
AI Is Taking Over Hospitals
This is health care’s Uber moment.
Treatment Response Optimized Clinical Decision Support AI System via Digital Twin Simulation
arXiv:2606.17405v1 Announce Type: new Abstract: Clinical decision support AI systems (CDSASs) must adapt to evolving patient conditions in real-time while adhering to strict safety constraints. We present an online adaptive framework that integrates Treatment Effect (TE) estimation to quantify clinical benefits, a patient Digital Twin (DT) to simulate treatment trajectories, and Reinforcement Learning (RL) for sequential decision-making. The AI system is initially trained on historical medical records and operates in a continuous learning loop. To ensure safety, a rule-based module monitors vital signs and blocks contraindicated treatments. Cases with strong internal model disagreement are flagged for clinician review, simulated in our experiments via a pre-trained outcome model. We validate our framework using both a synthetic clinical simulator and a real-world ovarian cancer dataset from The Cancer Genome Atlas (TCGA). In both simulated and clinical settings, our method demonstrated superior effectiveness and stability in recommending treatments compared to standard computational baselines. Furthermore, the AI system maintains low latency and requires expert consultation for only a minority of cases in our experimental validation, demonstrating its potential as a safe, clinician-supervised tool for personalized medicine that continuously improves through practical use.
From Growth to Proof: How Behavioral Health CFOs and CEOs Should Be Thinking About Technology Investments in 2026 - ContinuumCloud
Behavioral health leaders in 2026 face a new reality: proving measurable outcomes is now non-negotiable. The era of unchecked growth is over, and every decision - especially technology investments - must deliver clear results in three areas: cutting costs, securing revenue, and improving care ...
AI medical tools match or surpass doctors for advice
Two health models displayed clinical value across a range of diagnostic and treatment decisions, studies show
AI and brain-computer interface allow speechless ALS patient to work a full-time job
A UC Davis research team developed a machine learning method that translates brain activity into sentences with 92% accuracy for an ALS patient.
Informative Missingness to Generate Irregular Clinical Time Series
arXiv:2606.17106v1 Announce Type: cross Abstract: Laboratory tests in electronic health records are collected irregularly, and the absence of a test order can be as informative as the measurement itself. Such missingness reflects clinicians' decisions and patient physiology, making it important to model it directly rather than treat it as a preprocessing artifact. Here we present a diffusion-based approach for generating clinical time series that jointly models laboratory values and their observation patterns using the public Data Analytics Challenge on Missing Data Imputation (DACMI) benchmark derived from MIMIC-III. To preserve realistic sampling, we align chart times into 4-hour intervals and segment admissions into 7-day windows, producing trajectories that pair each lab value with a corresponding observation indicator. Standard transformations and normalization are applied to stabilize training. Our method extends the TimeDiff framework to learn continuous lab values and discrete missingness patterns through complementary diffusion objectives. Experiments show that the generated data closely match real patient trajectories across individual lab distributions and joint value-missingness embeddings, demonstrating that diffusion models can capture clinically meaningful dependencies between patient physiology and clinicians' testing behavior under MNAR-like (missing-not-at-random) missingness. These preliminary results indicate that our model can serve as an initial component toward developing clinical foundation models. By producing synthetic priors that preserve key physiology-missingness relationships, this work motivates the subsequent training of Prior-Data Fitted Networks capable of leveraging informative missingness, which we will investigate in the extended work.
SpeechDx: A Multi-Task Benchmark for Clinical Speech AI
arXiv:2606.17339v1 Announce Type: new Abstract: Speech offers a uniquely informative window into health by simultaneously engaging neurological, motor, respiratory, and vocal systems. Current clinical speech AI methods have largely progressed through isolated condition-specific studies, making results difficult to compare and generalization difficult to assess. We introduce SpeechDx, a large-scale benchmark for clinical speech AI spanning 12 datasets and 27 tasks across diverse health conditions. To enable evaluation across shared clinical mechanisms, SpeechDx structures tasks by the stage of speech production they disrupt: conceptualization, formulation, and articulation. The benchmark tests generalization by including tasks with limited labeled data and evaluating the same health condition across multiple datasets, distinguishing clinically meaningful patterns from dataset artefacts. We systematically evaluate 12 state-of-the-art audio encoders across all tasks and under zero-shot cross-condition transfer. Results show that large-scale speech models represent the strongest overall baselines, domain-specific models improve performance only on closely matched tasks, and no current representation generalizes reliably across the clinical speech landscape. SpeechDx establishes a shared evaluation framework for tracking progress toward general-purpose clinical speech representations
Patients With Personality: Realistic Patient Simulation through Controlled Diversity and Selective Disclosure
arXiv:2606.17441v1 Announce Type: cross Abstract: Simulating realistic patient interactions is a key requirement to testing clinical applications of LLMs at scale without time-consuming and expensive user studies. However, existing approaches often lack realism and controllability, often oversharing information unprompted, and failing to capture the wide variability of patient behavior. Here, we introduce PatientsWithPersonality (PWP), a patient simulation framework that generates realistic yet diverse virtual patient responses through explicit personality parametrization over a latent patient state. Grounded in HEXACO, a six-dimensional personality space used to quantify and parameterize human behavioral traits, our approach enables fine-grained control over conversational style, cooperativeness, and information disclosure within a unified framework. In a clinician evaluation, PWP is judged nearly as realistic as recorded human actors and clearly ahead of prior simulators, while being flagged as "too informative" far less often. Conditioning on HEXACO axes yields personas whose configured traits are recoverable by both clinicians and an autorater, span a substantially wider behavioral footprint than the closest baseline, and prevent oversharing. Altogether, our framework paves the way for more accurate and informative LLM benchmarking through our realistic and steerable patient simulator.
Palantir NHS claims rely on just a few hospitals
Data shows some trusts have delivered fewer operations since adopting the US company’s technology
Fusion is not one-size-fits-all: Cross-Modal Representation Alignment for Time-to-Event Modeling
arXiv:2606.15038v1 Announce Type: new Abstract: Accurate time-to-event (TTE) prediction from multimodal clinical data remains challenging due to modality imbalance and distribution shift. We introduce a foundation model-driven framework for cross-modal representation alignment between CT imaging and longitudinal EHR data, designed to generalize across tasks and institutions. CT and EHR modalities are encoded independently using domain-specific foundation models and aligned in a shared latent space through four principled fusion strategies: late fusion, contrastive alignment, cross-attention, and co-attention. We evaluate two clinically distinct TTE tasks: pulmonary embolism (PE) mortality and cardiovascular disease (CVD) outcomes, on large-scale multi-institutional cohorts (PE: N=3,099 train; 1,098 internal; 435 external; CVD: N=2,951 train; 837 internal; 682 external). Fusion consistently improves concordance index by 1.5-5.4% over unimodal baselines when modalities contribute comparably. Overall, contrastive multimodal fusion, particularly with CLMBR representations, provided the most consistent and statistically robust improvements, especially for PE mortality prediction. For MACE, cross-attention (one-hot) achieved the highest internal performance and image-guided co-attention achieved the best external performance. We therefore introduce a generalizable foundation model-based cross-modal alignment framework and provide the first systematic analysis of fusion behavior under modality imbalance in TTE prediction. Our results establish task-aware multimodal alignment as a necessary design principle for robust generalization and scalable clinical deployment.
Metric Match: A Subset Selection Approach to Evaluating LLM Judge Reliability
arXiv:2606.15029v1 Announce Type: new Abstract: LLM judges are used to reduce the need for costly human labor in evaluating open-ended text generation. However, the reliability of these judges depends critically on their alignment with human raters -- a property that itself depends on costly human annotations. In this work, we develop a method (Metric Match) for estimating correlation-based reliability metrics of LLM judges from limited annotations. Metric Match selects a subset of samples for human annotation such that the subset matches the population reliability metric with respect to acquired synthetic labels. We empirically show that Metric Match achieves a win-rate of 0.838 against random subset selection across four different correlation metrics and 15 datasets, with an 18.7% decrease in average estimation error and reduces annotation needs by 32.5%. We provide a cost model and highlight a medical case study where our method saves $1,041.67 compared to random selection for expert annotation. Further, we shift our task from reliability estimation to reliability classification of whether a given judge is above a deployment threshold, outperforming random selection with Metric Match. All project code is publicly available, and we additionally provide an installable package for ease of use.
AHA provides recommendations to CMS on proposed rule for interoperability standards and prior authorization for drugs | AHA News
The AHA provided comments June 15 to the Centers for Medicare & Medicaid Services on its proposed rule establishing electronic standards for drug prior authorizations.
Contributor: Prior Authorization in 2026—CMS Is Rebuilding the Operating Model | AJMC
The proposed rule on interoperability standards and prior authorization could lead to significant change in CMS.
When Sample Selection Bias Precipitates Model Collapse
arXiv:2606.13732v1 Announce Type: new Abstract: The proliferation of recursive training on synthetic data can alleviate data scarcity but risks model collapse, where repeated training erodes distributional tails and homogenizes outputs. Data selection is widely viewed as a remedy, yet its reliability depends critically on the reference distribution used by the verifier. We show that in low-resource verification regimes, where each verifier observes only a small, fragmented, and biased slice of the target manifold, selection itself becomes biased. This situation naturally arises in low-resource data silos such as healthcare consortia or proprietary financial institutions, where raw data cannot be pooled and local references are inherently incomplete. As a result, selection preferentially retains samples aligned with the local manifold while pruning globally relevant tail modes, turning from a safeguard against collapse into a mechanism that precipitates it. We theoretically prove that such siloed selection accelerates collapse and induces power-law diversity decay. As an initial mitigation, we construct Wasserstein proxy references from multiple silos without sharing raw data. Empirical results confirm that local-reference selection fails on skewed distributions, whereas collaborative proxy references mitigate diversity degradation, suggesting that recursive synthetic-data pipelines require particular caution when real-data coverage is fragmented or scarce.
The Palantir controversy is a block on NHS progress
Better patient outcomes are at risk in the backlash against American tech
Deployment-Centered Evaluation: Predicting Query-Level Rejection Risk in a Clinical LLM System
arXiv:2606.12702v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly integrated into clinical systems, making it essential to evaluate the real-world utility of these systems. However, static benchmarks tend to measure correctness rather than user acceptance, aggregate performance across queries, and require densely annotated datasets -- leading to major blind spots for evaluating clinical systems. In this work, we perform a deployment-centered evaluation of an LLM system embedded within electronic health records at an academic medical center, where user feedback is sparse but closely reflects the deployment conditions. Specifically, we train a pre-response classifier that estimates the risk that a future interaction will result in the user rejecting the LLM response, based on query content and deployment-specific context available before generation. We conduct a prospective analysis of our model over 4.5 months of user feedback, finding that our prediction model achieves an AUROC of 0.719. Further, we estimate the benefit of such predictions in two downstream use cases (guardrail triggering and abstention). Our key conceptual insight is that making use of deployment-specific context (i.e., the provider type, department name, language model used for response), as opposed to only query content, improves the ability to predict whether the user will reject the system output. Altogether, our empirical case study demonstrates the feasibility of predicting user rejection using deployment-specific context, opening the door to targeted guardrails.
Benchmarking AI Agents for Addressing Scientific Challenges Across Scales
arXiv:2606.12736v1 Announce Type: new Abstract: AI agents are increasingly being developed to accelerate scientific discovery, yet their practical capabilities in real research settings remain poorly understood. Existing benchmarks for AI agents rarely capture the complexity, heterogeneity, and extended reasoning required by scientific work, whereas benchmarks for scientific tasks often reduce research to static, direct problems and provide limited support for interactive evaluation. Here, we introduce SciAgentArena, a systematic benchmark for evaluating AI agents in real-world scientific research scenarios drawn from emerging needs across multiple domains. SciAgentArena comprises approximately 200 tasks with stepwise verification and an interactive, agent-agnostic environment for assessing diverse AI agents. Using this benchmark, we find that current agents can contribute effectively to well-specified data-analysis workflows, particularly when the task structure and evaluation criteria are clear. However, their performance remains uneven across scientific contexts: agents struggle to generate genuinely novel insights, sustain self-directed exploration, and formulate robust solutions for open-ended research questions. We further characterize common failure modes across agents and identify opportunities for improving their reliability, autonomy, and scientific reasoning. Together, SciAgentArena provides a practical framework for measuring progress in AI agents for science and for guiding the design of future agents capable of addressing complex scientific challenges. Full codes, tasks, and datasets can be accessed via this link: https://sciagentarena.github.io/.
Reducing the Complexity of Deep Learning Models for EEG Analysis on Wearable Devices
arXiv:2606.12742v1 Announce Type: new Abstract: Wearable healthcare devices are the fastest-growing Internet of Things (IoT) sector. Many automated healthcare services rely on two crucial biological signals, namely ECG and EEG, which reflect the activity of the heart and brain, respectively. Although deep neural networks are considered the primary way to process and analyze these signals, the very tight energy and computational power constraints in wearable devices are far below the computational, energy, and memory bandwidth demands of DNN models, thereby impeding the deployment of deep learning in many practical wearable services. This paper investigates the feasibility of deploying state-of-the-art DNN models in resource-constrained wearable devices. Notably, we explore the trade-off between accuracy and computational complexity of DNNs when parameter quantization and electrode reduction methods are used. Our investigation centers on several state-of-the-art DNN models designed for EEG signal analysis, specifically for detecting epileptic seizures. Our findings demonstrate that, when applied judiciously, these techniques can significantly reduce the complexity of the DNNs under consideration with minimal adverse effects on accuracy. These results reveal the explicit trade-offs between accuracy and complexity reduction encountered when adapting DNN-based online EEG analysis for wearable devices.
Revisiting the ABCs of Working with AI: A Replication with Radiologists
arXiv:2606.12585v1 Announce Type: new Abstract: Artificial intelligence (AI) systems increasingly assist human experts, but the consequences of AI assistance on productivity can be heterogeneous. Caplin, Deming, S. Li, Martin, Marx, Weidmann, and Ye (2025b) provide evidence that two characteristics, ability and belief calibration, help to determine the returns to AI assistance. This note shows th
Can AI Agents Synthesize Scientific Conclusions?
arXiv:2606.11337v1 Announce Type: new Abstract: Scientific AI agents increasingly retrieve evidence, reason across sources, and synthesize conclusions used in consequential decisions. Yet, their ability to do so in high-stakes domains such as health remains unclear. We introduce SciConBench, a large-scale live benchmark of 9.11K questions and expert-written conclusions from systematic reviews to
Amsterdam’s OurMind raises €2.1 million to reduce healthcare admin burden with AI
OurMind, an Amsterdam-based startup developing AI solutions to reduce administrative burdens on healthcare providers and to reduce burnout, has raised €2.1 million to scale and expand its platform. The round was led by 4impact capital, with a group of general practitioners and medical specialists also making a significant contribution. Paul Koning, a former orthopaedic surgeon […]
Lung-R1: A Knowledge Graph-Guided LLM for Pulmonary Diagnostic Reasoning
arXiv:2606.11675v1 Announce Type: new Abstract: Diagnosing pulmonary diseases requires integrating heterogeneous evidence amid phenotypic variability and cross-disease overlap. Although large language models (LLMs) have shown progress on pulmonary knowledge question answering (QA) and information-processing tasks, reliable pulmonary diagnosis requires patient-specific, relation-aware reasoning over electronic medical record (EMR) evidence rather than isolated knowledge recall. We define this gap between pulmonary knowledge and case-level diagnostic reasoning as the Pulmonary Knowledge-to-Diagnosis Gap. To address it, we introduce LungKG, the first structured pulmonary knowledge graph for diagnostic knowledge organization and record-grounded reasoning. LungKG contains 59,038 nodes and 164,308 edges across 15 entity types and 112 relation types, serving as both a reusable pulmonary knowledge resource and the foundation for LungKG-guided model adaptation. Built on LungKG, we propose Lung-R1, a LungKG-guided pulmonary LLM trained through KG-constrained reasoning-chain construction and KG-guided reinforcement learning. In a 20-system evaluation, Lung-R1-14B achieves state-of-the-art performance across Choice, Pulmonary-QA, and EMR Diagnosis, reaching an EMR Diagnosis score of 4.3583 and surpassing the strongest non-Lung-R1 baseline by 0.1476 points. These results demonstrate the value of LungKG-guided training for EMR-based pulmonary diagnosis.
Cost per sample? Try cost per attempt
PARTNER CONTENT Your genomics pipeline is probably failing 30% of the time and you're paying for all of it
Abridge wants to be the operating system for medicine—and NVIDIA and Eli Lilly are helping build it
The $5.3 billion ambient AI startup is expanding from clinical notes into billing, drug trials, and real-time insurance claims.
Colorado AI Law Faces DOJ, Elon Musk Challenge | Telehealth.org
Elon Musk’s xAI and the DOJ challenged Colorado’s AI law, raising questions about healthcare AI regulation, algorithmic discrimination, and state oversight.
Supervised Fine-tuning with Synthetic Rationale Data Hurts Real-World Disease Prediction
arXiv:2606.10279v1 Announce Type: new Abstract: Supervised fine-tuning with synthetic rationale data is widely assumed to improve language model performance on clinical prediction tasks by teaching models not just what to predict but why. We test this assumption on five-year Alzheimer's disease and related dementias (ADRD) prediction from longitudinal health histories. Across a large-scale controlled experiment of 504 configurations, we find that rationale-based SFT consistently and substantially hurts prediction performance relative to label-only fine-tuning. The degradation persists across model families and data scales, and is not resolved by using a reasoning-oriented base model. Crucially, the failure is not explained by poor rationale quality: human expert annotation confirms that the generated rationales are medically accurate and faithfully grounded in patient-specific evidence, and few-shot experiments show that the same rationales improve performance when used as inference-time demonstrations rather than training targets. We identify the root cause as a structural conflict between narrative plausibility and discriminative optimization. We hope our work paves the path toward a more precise understanding of when and how rationale-based supervision helps and when it does not, guiding the responsible development of language models for high-stakes clinical prediction.
The Empirically Grounded Adaptive Virtual Patient for Psychotherapy Training: Disclosure That Responds to Therapist Micro-Skills
arXiv:2606.10051v1 Announce Type: new Abstract: Simulated patients offer a scalable way to train psychotherapy micro-skills such as empathic responding and exploratory probing, but current systems either follow fixed scripts or rely on LLMs that drift unpredictably over long sessions. We present the Adaptive Virtual Patient (AVP), which adapts its disclosure behavior -- from guarded, through moderate openness, to full disclosure -- in response to trainee skill. The AVP is grounded in a structural equation model fit to nearly 2{,}000 hours of real-world psychotherapy transcripts, which quantifies how therapist empathy and exploration shift a patient's openness over time. An LLM generates the AVP's utterances conditioned on a disclosure level that the dynamics module updates each turn. In an evaluation with 20 clinicians and trainees over 80 sessions (1{,}033 turns), the AVP's disclosure rises in response to therapist empathy and exploration, while a prompt-only baseline stays flat; ablations confirm that the empirically motivated parameterization outperforms alternatives, with exploration carrying most of the adaptive signal.
"Where is this coming from?" Uncovering Trustworthiness Ideals in AI-powered Peripartum Information Seeking
arXiv:2606.10158v1 Announce Type: new Abstract: AI-powered tools increasingly promise to fill information gaps in health, especially in domains like maternal and reproductive health that demand timely, accurate, and actionable information. This is extremely important, as the United States leads peer nations in preventable deaths, with stark racial disparities. However, current AI and NLP-powered systems aim to improve access to vetted maternal health information by routing user queries to a factual response while under-specifying the socio-technical governance structures that shape trust, use, and harm in practice. We report findings from four synchronous focus groups ($n=24$) with three stakeholder groups central to peripartum information support: birthing people, clinicians, and health workers (e.g., doulas, social workers, community health workers) exploring topics around information seeking, experience with current clinical infrastructure, misinformation, and an AI-enabled factual answering tool design probe. Our inductive analysis surfaces a central finding: in high-stakes health contexts shaped by historical inequities, trustworthiness must be inspectable and not asserted. While stakeholders diverge on what makes information credible, they converge on the need for transparency, recourse, and ecosystem complementarity. Based on the discussions, we identify four themes and governance requirements: (1) support for social and identity-based sensemaking, (2) pluralistic verification practices, (3) inspectable governance with recourse mechanisms, and (4) ecosystem-aware integration that avoids shifting burden. Building on these findings, we propose design artifacts that are mistrust-aware and promote principled governance mechanisms for transparent, pluralistic AI systems. Finally, we discuss the implications of our findings for expanding human-AI evaluations and improving the transparency of deployed AI systems.
Clinicians are embracing AI faster than hospitals can handle, report finds | Euronews
Healthcare professionals are saving weeks of working time each year thanks to AI, but health systems are struggling to keep pace with demand, according to a new report by Philips.
AI in healthcare 2026: efficiency gains and unresolved costs
AI in healthcare 2026 is delivering real clinical gains, but health systems still face the question of who truly benefits as reimbursement models lag behind.
Why oncology is becoming healthcare AI's toughest test | Healthcare IT News
As health systems expand their artificial intelligence projects, tackling areas such as ambient charting and pharmacovigilance, cancer care is one area that's exposing data governance challenges that may determine success or failure for enterprise AI.
Automatic Extraction of Structured Information from Brain MRI Reports Using an Open-Weight Large Language Model
arXiv:2606.07721v1 Announce Type: new Abstract: Objectives: Automatic data extraction from free-text radiology reports enables large-scale research, but few studies assessed the performance of large language models (LLMs) on Dutch neuroradiology reports. Methods: We analyzed 947 brain MRI reports from a tertiary memory clinic (2016-2021), authored by consultant neuroradiologists. Trained medical students annotated thirty variables; 100 reports were double-annotated to assess inter-rater reliability. We evaluated the performance of the open-weight LLM LLaMA 3.1 using different languages (Dutch vs. English translation) and few-shot prompting with different example selection strategies. Performance was evaluated using balanced accuracy for categorical variables, accuracy and mean absolute error for counts, and text similarity for free-text. Metrics were computed across 10 random splits of the 947 reports. Results: LLaMA 3.1 demonstrated high zero-shot performance for visual rating scores (mean [95%-CI]): Medial Temporal Atrophy: 90% [77-100%] on the left and 96% [94-99%] on the right, Global Cortical Atrophy: 87% [83-91%], and Fazekas: 94% [93-96%]. Microbleed mentions were detected with 93% accuracy [92-95%] and infarct mentions with 82% [80-84%]. Text similarity for lesion location reached 0.95 [0.95-0.96]. Performance was lower for numerical variables: 80% [78-82%] for the number of microbleeds and 66% [63-68%] for infarcts. English translation yielded comparable results. Few-shot prompting improved performance for numerical variables, achieving 92% [90-93%] for microbleeds and 81% [77-85%] for infarcts using structural similarity-based selection. Conclusion: LLaMA 3.1 shows strong potential for extracting data from Dutch neuroradiology reports. Few-shot prompting enhances performance for numerical variables, whereas challenges remain for location-specific variables.
Reconstructing and forecasting disease trajectories of patients with Alzheimer's disease using routine data in resource-constrained settings
arXiv:2606.07798v1 Announce Type: new Abstract: Alzheimer's disease is a progressive neurodegenerative disorder, and its progression varies substantially across patients. Existing work aims to forecast patients' future cognitive state, with minimal focus on reconstructing the state from past visits. Furthermore, in current research, quantifying predictive uncertainty remains underexplored and relies on costly modalities such as MRI, PET, and CSF, limiting their deployment in resource-limited settings. In this research, our primary objectives are: First, bidirectional prediction of cognitive scores from irregular visits to present the complete disease trajectory. Second, to enable interpolation and extrapolation capabilities to assist clinicians in informed prognostic decision making, and third, to provide a well-calibrated uncertainty estimate for all predictions, and finally, to achieve the objectives using the modalities available during routine visits. We propose a unified framework, GNOVA: A GRU-Neural ODE Variational Autoencoder. The architecture combines a Gated Recurrent Unit encoder and a Neural ODE decoder within a variational autoencoder framework. In our work, we forecast the CDR-SB and MMSE Scores. The GRU encoder allows for any number of inputs at any time point. The Neural-ODE decoder performs continuous estimation, allowing interpolation and extrapolation at any desired time point. The Variational autoencoder allows for uncertainty estimation in predictions. We worked with 1,727 patients from the ADNI dataset over 10 years; the model achieved mean absolute errors of 1.35 and 2.28 for CDR-SB and MMSE scores, respectively, without requiring any neuroimaging or biomarker data. Feature-ablation studies revealed that age, BMI, and APOE4 status were strong predictors. The proposed framework enables the reconstruction of incomplete patient histories and the anticipation of future cognitive states.
PathoSage: Towards Multi-Source Evidence Adjudication in Pathology via Experience-Aware Agentic Workflow
arXiv:2606.07549v1 Announce Type: new Abstract: Recent advances in Multimodal Large Language Models (MLLMs) and agent workflows have shown strong promise for computational pathology, yet reliable patch-level reasoning remains challenging. End-to-end pathology MLLMs often hallucinate morphological features, while recent agentic systems usually merge tool outputs and retrieved knowledge into a shared context, making decisions vulnerable to conflicting evidence and context contamination. We propose PathoSage, a three-stage framework that explicitly separates knowledge retrieval, evidence collection, and evidence adjudication for patch-level pathology multimodal reasoning. Its core component, Structured Evidence Deliberation, independently evaluates heterogeneous evidence from tools, performs conflict analysis, and generates the final judgment in a fresh context to reduce anchoring bias. We further introduce a training-free Beta-Bernoulli experience system with continuous credit assignment to model long-term tool reliability and construct similarity-weighted priors for future tool use. Experiments show that PathoSage effectively mitigates VQA hallucinations and classifier disagreement, outperforming strong pathology MLLM and agentic baselines. Our results highlight explicit evidence adjudication and reliability-aware tool modeling as key ingredients for robust pathology agents.
AI in Healthcare Emerges as High-Growth Opportunity, with Market Projected to Reach US$ 146.3 Billion by 2031 - Latest Report by Wissen Research
/PRNewswire/ -- The global AI in healthcare market is projected to grow from USD 20.2 billion in 2025 to USD 146.3 billion by 2031, registering a CAGR of 39%...
Beyond Prediction: Longitudinal Reasoning in EHR-Integrated Clinical AI
arXiv:2606.08413v1 Announce Type: new Abstract: We present a structured analysis of how contemporary clinical AI systems integrate electronic health record (EHR) data and the extent to which they support longitudinal clinical reasoning. Drawing on a curated corpus of clinical natural language processing (NLP) and EHR-integrated systems, we develop a coding framework that captures both technical integration strategies and reasoning-relevant representational features, such as trajectory modeling, cross-encounter synthesis, longitudinal analysis, and absence reasoning. We also elicited the experiences of three physicians in their EHR use, including what strengths and weaknesses they found with their institution's current EHR system(s). Our analysis shows that while many systems incorporate EHR data, they predominantly operate on encounter-level or aggregated representations, with limited support for explicit temporal reasoning across patient histories. Reasoning-relevant structures are inconsistently represented, and evaluation paradigms remain largely focused on predictive performance instead of longitudinal interpretability. We argue that current approaches treat EHR data as a static input rather than a substrate for ongoing clinical reasoning, and we outline a framework for understanding how future systems might more effectively align with the temporal and interpretive structure of clinical practice.
DIYHealth Suite: Dataset, Model, and Benchmark for Health Management at Home
arXiv:2606.07542v1 Announce Type: new Abstract: Generative AI is reshaping healthcare, yet most existing advances rely on hospital-grade devices, which limits their accessibility and potential for health management outside clinical settings. With the proliferation of portable devices and telemedicine, healthcare is shifting toward home-based Diagnosis-It-Yourself (DIY) care. Despite this promise, several distinctive challenges remain: (i) home-collected data are heterogeneous, exacerbated by the absence of standardized large-scale datasets; (ii) models require adaptation to variable task demands and evolving individual conditions; (iii) the broad spectrum of home care tasks lacks a unified benchmark for systematic evaluation. In this paper, we present DIYHealth Suite, a comprehensive framework designed to address these challenges through a tailored dataset, model, and benchmark. We first curate DIYHealth-900K, a large-scale multimodal dataset capturing diverse real-world home care scenarios. Building on this, we propose DIYHealthGPT, an adaptive foundation model for home-based health management, powered by the novel Hybrid Hyper Low-Rank Adaptation technique. Finally, we establish DIYHealthBench, the first benchmark to evaluate foundation models on home care tasks. Extensive experiments demonstrate that DIYHealthGPT delivers state-of-the-art performance over both general-purpose and medical-specific baselines on 11 home care tasks in both open-QA and closed-QA settings, laying the groundwork for the next generation of personalized health management at home.
Doctors and NHS could be sued for mistakes made by AI tools, report warns
Medical Protection Society calls for law to be overhauled to help medics avoid liability for errors made by technology Doctors and the NHS could be sued for medical negligence over mistakes made by artificial intelligence tools used in diagnosing patients and suggesting their treatment, ministers are being warned. Under the law as it stands, medics and the health service can be held liable for patients being harmed or dying even if it was AI that made the errors that resulted in their suffering. Continue reading...
Have a Thorny Medical Question? Your Doctor May Be Using A.I. for That.
OpenEvidence, a fast-growing start-up, is using artificial intelligence to help doctors find answers to clinical questions for diagnosis and treatment.
Evidence-Based Intelligent Diagnostic and Therapeutic Visualization System with Large Language Models: Multi-Turn Interaction and Multimodal Treatment Plan Generation
arXiv:2606.06869v1 Announce Type: new Abstract: Aim: Existing AI-assisted traditional Chinese medicine diagnostic tools suffer from opaque reasoning processes, passive interaction, and limited treatment plan presentation. This study proposes a knowledge-enhanced visual diagnostic system to improve the transparency and interpretability of syndrome differentiation and treatment. Methods: The system is built upon a Neo4j knowledge graph comprising 241 syndromes, 1,263 symptoms, and 2,485 relations. It incorporates a four-stage symptom matching pipeline (exact, semantic, fuzzy, and large language model verification), an information gain-driven proactive questioning strategy optimized with genetic algorithms, and a multimodal treatment presentation integrating artificial intelligence-generated illustrations, three-dimensional meridian-acupoint models, and evidence-based literature. Results: Knowledge graph constraints reduced non-standard outputs by 32%. Case studies validated the effectiveness of the interactive workflow across patient self-assessment, clinician-assisted diagnosis, and traditional Chinese medicine education. Automated paired-comparison evaluation across 30 cases further demonstrated significant improvements in diagnostic trust (Cohen's d = 1.82, p < 0.001), reduced cognitive load (improvements in four of five dimensions), and higher credibility of evidence-based references (4.21 vs. 2.95). Conclusions: The proposed system enhances the transparency of traditional Chinese medicine diagnostic reasoning and the interpretability of treatment plans through knowledge graph-driven visualization and multimodal interaction, offering a practical solution for trustworthy artificial intelligence-assisted traditional Chinese medicine applications.
AI ‘digital twins’ are transforming heart care but will they work for women?
Sumesh Sasidharan of the Faculty of Medicine at Aix-Marseille Université explores how transformations in medtech may not impact all patients equally. Read more: AI ‘digital twins’ are transforming heart care but will they work for women?
An interpretable and trustworthy AI framework for large-scale longitudinal structure-pain association studies using data from the Osteoarthritis Initiative (OAI)
arXiv:2606.05357v1 Announce Type: new Abstract: Purpose: To develop an interpretable and trustworthy AI framework that combines deep learning based MRI Osteoarthritis Knee Score (MOAKS) prediction with interpretable statistical modeling to study structure-pain relationships at scale using data from the Osteoarthritis Initiative (OAI). Materials and Methods: We first developed a deep learning framework to predict MOAKS features directly from knee MRIs and incorporated conformal prediction to provide prediction uncertainty quantification. This uncertainty-aware strategy enables explicit filtering of model outputs, retaining only high-confidence MOAKS predictions at the knee level. Second, we applied a longitudinal latent class mixed model (LCMM) to examine associations between key structural abnormalities and four complementary knee pain measurements. Results: Among the three MRI-defined abnormalities (i.e., bone marrow lesions (BML), cartilage loss (CART), and meniscal extrusion (ME)), our framework substantially improved the Matthews correlation coefficient (MCC) and some other metrics. For example, MCC increased from 0.69 to 0.91 for BML, from 0.45 to 0.80 for CART, and from 0.59 to 0.89 for ME. Using these high-confidence predictions, we expanded the sample size to 2,175 knees for the LCMM analysis. Two distinct pain trajectories were identified (rapid and stable pain progression). The estimated odds ratios (95% CI) for the rapid progression group were 1.62 (1.12-2.35) for BML, 1.83 (1.24-2.70) for CART loss, and 2.50 (1.75-3.57) for ME. Conclusion: These results highlight the importance of these structural abnormalities as risk factors for pain and functional progression in osteoarthritis.
Meta Targets Health-Focused AI
Meta is prioritizing health as a differentiator for its AI strategy, integrating these capabilities into platforms like Instagram and WhatsApp.
AI in Healthcare: Benefits, Use Cases, & Future Trends in 2026
This blog explains the role of AI in healthcare, including its advantages, types, use cases, and key considerations before implementing the technology.
Inside the Trump-backed push to bring AI doctors into American medicine - The Washington Post
The administration is laying the groundwork for chatbots that can diagnose illness and prescribe medicine, but physicians say AI can introduce more problems.
Health Brief: The Trump-backed push to put AI in the exam room - The Washington Post
Plus: The new legal challenge to ACA marketplace rules
London’s Semble raises €34.7 million Series C to scale its healthcare management platform for outpatient providers
Semble, a London-based HealthTech startup helping outpatient providers coordinate care and manage the entire patient journey, has secured a €34.7 million (£30 million) Series C funding round. The round was led by European growth investor Revaia, with participation from a second new investor, Partech, alongside continued backing from existing investors Mercia Ventures and Octopus Ventures. […]
Australian healthcare AI adoption slowed by maturity gaps
Most Australian healthcare providers are stuck in pilot mode as weak data, governance and operating models limit wider AI rollout.
Germany puts AI and EHDS at heart of health strategy | ICT&health
Germany places AI, health data and EHDS at the heart of its updated digital health strategy, outlining a roadmap for healthcare transformation by 2030.
New HSCC guide addresses cybersecurity risks specific to healthcare AI | Healthcare IT News
That guide addresses the need for ... and risk management of third-party AI systems and vendors. The suite of HSCC works reinforces its five-year strategic plan, which sets a goal to upgrade the diagnosis of healthcare cybersecurity from "critical" to "stable condition" by 2029 to reduce patient safety ...
23andMe Is Back as Nonprofit Aiming to Reach 100 Million Users
The founder of 23andMe Research Institute wants to reach 100 million users, an ambitious goal after the seller of DNA testing kits emerged from bankruptcy as a nonprofit.
Top 6 worries about healthcare AI among clinicians and patients in 2026
AI is now as much a part of U.S. healthcare as any other technology category in wide use across the sector. However, like no other technology, its role is “being actively shaped, not passively adopted” by clinicians and patients alike.
Traj-Evolve: A Self-Evolving Multi-Agent System for Patient Trajectory Modeling in Lung Cancer Early Detection
arXiv:2606.02812v1 Announce Type: new Abstract: Modeling patient trajectories from longitudinal electronic health records (EHRs) requires reasoning over sparse, noisy, and long-context multimodal sequences. Existing LLM-based multi-agent systems address context length but process patients in isolation, failing to mirror how clinicians leverage accumulated experience from similar prior cases. We present Traj-Evolve, a self-evolving multi-agent system with two complementary evolving mechanisms. First, an Experience Pool (ExPool) acts as a non-parametric memory, indexing rejection-sampled reasoning traces to retrieve similar patients as few-shot contexts. Second, multi-agent reinforcement learning (MARL) via reward-ranked fine-tuning parametrically optimizes inter-agent and agent-memory collaboration. A leave-one-out cross-retrieval strategy unifies the two, aligning training- and inference-time behavior under retrieval augmentation. On a lung cancer prediction task utilizing up to five years of multimodal EHRs, Traj-Evolve outperforms 9 strong baselines on the overall population and a challenging never-smoker population. Analysis of the evolving dynamics highlights three key findings: (1) expanding the ExPool shifts optimal retrieval from diverse to specific samples; (2) under MARL, the manager agent's prediction loss converges quickly while the worker agents' temporal reasoning continues to benefit from more verified patients; and (3) the two mechanisms are complementary on the predicted risk, where ExPool improves specificity while MARL improves sensitivity.
Large AI Models in Dental Healthcare: From General-Purpose Systems to Domain-Specific Foundation Models
arXiv:2606.02914v1 Announce Type: new Abstract: Background: Oral diseases affect nearly 3.5 billion people worldwide, yet the comparative clinical potential of large-scale AI models in dentistry remains poorly understood. Three distinct model categories have emerged: language-generative models, discriminative vision foundation models, and dental-specific foundation models, with no unified review examining their relationships and collective limitations. Methods: Following PRISMA-ScR guidelines, we systematically searched four databases (PubMed, Google Scholar, Scopus, arXiv), screened independently by two reviewers. After applying inclusion/exclusion criteria, 97 studies (2020-2026) were included. We propose a two-dimensional classification framework organizing models by architectural paradigm and dental specialization degree. Results: Language-generative models excel at text-based tasks (clinical reasoning, licensing exams, patient communication) but show inconsistent performance on image-dependent diagnostics. Adapted SAM and CLIP variants achieve strong tooth segmentation and lesion detection results. Dental-specific models (DentVFM, DentVLM, OralGPT) demonstrate strongest performance on complex multimodal tasks. Integrated pipelines consistently outperform single-model approaches. A data asymmetry is observed: dental-specific pretraining concentrates almost entirely in the vision domain, reflecting scarce large-scale dental text corpora. Conclusions: General-purpose and dental-specific models play complementary roles; the most effective systems combine both within structured pipelines. Safe autonomous deployment requires resolving three persistent barriers: hallucination in generative models, limited annotated dental datasets, and absent standardized clinical evaluation benchmarks.
ChatHealthAI: Aligning Electronic Health Record Representations with Large Language Models for Grounded Clinical Reasoning
arXiv:2606.02802v1 Announce Type: new Abstract: Large language models (LLMs) exhibit strong natural-language reasoning abilities for clinical decision support, but struggle to effectively model structured longitudinal electronic health records (EHRs). In contrast, EHR foundation models can learn predictive patient representations, yet lack interpretable language-based reasoning. To bridge this gap, we propose ChatHealthAI, a multimodal reasoning framework that aligns structured EHR representations from a pretrained EHR foundation model with the semantic space of a frozen LLM through a task-aware resampler. By integrating longitudinal patient representations with refined clinical event descriptions, ChatHealthAI enables clinically grounded natural-language reasoning while maintaining accurate patient prediction. We evaluated ChatHealthAI on three clinical predictive tasks from the EHRSHOT benchmark. Results show that ChatHealthAI improves reasoning quality and interpretability while preserving competitive predictive performance. These findings highlight the potential of integrating EHR foundation models with pretrained LLMs for interpretable clinical prediction.
Effect of Demographic Bias on Skin Lesion Classification
arXiv:2606.03214v1 Announce Type: cross Abstract: In this study, we evaluate the performance of skin lesion classification using ResNet-based convolutional models, focusing on the impact of demographic bias in training data, particularly variations in patient sex and age. We use linear programming to generate datasets with controlled demographic characteristics, allowing systematic investigation of bias effects. Three learning strategies are evaluated: a single-task model, a reinforcing multi-task model, and an adversarial learning scheme. Our sex-based analysis indicates that sex-specific training datasets optimise model performance. Notably, including male patients in the training data improved performance for the male subgroup, even in female-majority cases. Reinforcing and adversarial learning schemes narrowed or eliminated bias gaps in balanced and female-majority datasets. However, these strategies proved less effective in male-majority settings, where models continued to perform better for males than females. The two learning schemes showed marginal bias reduction compared to the baseline model in predominantly male patient populations. Age-based analysis demonstrates comparable baseline performance across the three model approaches, with performance declining across age categories. Younger groups consistently achieve the highest performance, regardless of training data distribution. Although balanced training yields optimal results for the youngest age category, performance decreases in older categories. We find that sex biases arise mainly from data imbalances, while age biases consistently favour younger groups regardless of distribution. These distinct mechanisms require targeted mitigation strategies. Additionally, cross-dataset validation on two external datasets revealed that domain shifts notably affect performance and patterns of demographic bias.
Preventive Care Disruptions and Emergency Hospitalizations
arXiv:2512.18342v4 Announce Type: replace Abstract: This paper studies whether interruptions to organized breast cancer screening lead to greater later use of emergency hospital care. It focuses on the first wave of COVID-19, when routine mammography was widely reduced across Europe, disrupting the usual screening pathway of early detection, follow-up testing, referral, and planned treatment. Using SHARE data from eight countries, the authors examine women aged 50 to 69, the main target group for organized screening programs. They estimate how mammography uptake affects all-cause overnight emergency hospitalization, interpreted as a broad measure of downstream strain on the health system after preventive care disruption. To address selection into screening, they use an instrumental variables strategy based on interview timing in Wave 9 interacted with cross-country differences in first-wave restrictions. The results suggest that pandemic-related declines in mammography increased later emergency hospitalization for screening-eligible women, while no such effect appears for women aged 70 and older.
Venture Capital & Startup Funding Roundup, June 2, 2026 - Tech Startups
Home health is a category where ... is that AI can turn rejected referrals into served patients by changing the economics of coordination. Investors appear to believe that the biggest defensibility in care delivery may come from owning both the workflow engine and the service line. ... Startup: Adaptive Innovations Investors: Felicis, Bain Capital Ventures, Optum Ventures, Sunflower Capital, Conviction, BoxGroup, SV Angels, Dorm Room Fund, Constellation, ...
Community Health Workers Are Right to Distrust AI Solutions - ICTworks
Four RCTs in LMICs across five ... for sector-wide deployment. The $60 million EVAH evaluation initiative is a start. Make LMIC-validated evidence a prerequisite for funding at scale, not a nice-to-have. Email a link to a friend (Opens in new window) Email ... Filed Under: Healthcare More About: ...
Wolters Kluwer’s Future Ready Healthcare survey: Rapid AI adoption in healthcare highlights worries, opportunities, for both patients and clinicians
Wolters Kluwer’s Future Ready Healthcare survey. Read the press release
Rehumanizing global health care with agentic AI
The global health care sector is under increasing strain. Decades of chronic underinvestment and constraints in recruitment have coincided with a surge in demand for services for aging populations. Gaps in provision are already taking a toll, with fragmented access to care and high rates of stress and burnout among staff. And it’s getting worse.…
AI adoption surges, but providers worry about deskilling | Healthcare Dive
Nearly three-quarters of clinicians said losing critical thinking or decision-making skills will be one of the greatest risks of adopting artificial intelligence, according to a survey by Wolters Kluwer Health.
HSCC publishes AI Cyber Governance guide to help healthcare providers manage emerging AI threats - Industrial Cyber
HSCC publishes AI Cyber Governance guide to help healthcare providers manage emerging AI threats, strengthen AI security oversight.
Joint Commission intros new voluntary AI responsibility certification | Healthcare IT News
The program is designed to recognize hospitals and health systems for adopting and implementing artificial intelligence tools with sufficient governance, monitoring processes and education programs in place.
Agentic AI In Healthcare: Smarter Care Through Intelligent Systems
Benefits of Agentic AI in healthcare, from intelligent diagnostics and workflow automation to better patient outcomes and reduced costs.
China has approved the world’s first invasive brain-computer chip—here’s what’s next
One day last October, sitting in the courtyard of his house in China’s Henan province, Dong Hui decided to see if he could hold a pen to write. Dong, 39, had sustained spinal cord injuries in a car accident six years earlier that left him paralyzed from the neck down. Slowly but determinedly, he wrote…
Driving global health equity with artificial intelligence: the global initiative on AI for health (GI-AI4H) | npj Health Systems
The Global Initiative on Artificial Intelligence for Health (GI-AI4H) is a World Health Organization-led collaboration with the International Telecommunication Union and the World Intellectual Property Organization to support safe, ethical, and equitable Artificial Intelligence (AI) adoption ...
Researchers Create First-of-Its-Kind Index of Evolving Policy Landscape Around Health Care AI | Mount Sinai - New York
The researchers analyzed 240 health ... efforts are accelerating worldwide, though no single, unified framework currently exists to guide how AI should be deployed, monitored, and governed in clinical settings....
Dutch gov't urged to critically examine whether AI can really solve healthcare problems | NL Times
It urged the Dutch government and parliament to critically examine whether AI truly offers real answers to the challenges in the sector, and whether using the technology is desirable. The CEG examined potential AI solutions for the widely recognized challenges in healthcare - staff shortages, ...
Guide issued for healthcare organizations on cyber governance frameworks for secure AI implementation | AHA News
The Health Sector Coordinating Council’s Cybersecurity Working Group has released a guide to help healthcare organizations establish cyber governance frameworks for secure artificial intelligence implementation. The guide addresses challenges in identifying and mitigating AI-specific cyber ...
EHRBench: An Automated and Reliable EHR-based Benchmark for Clinical Decision Making with LLMs
arXiv:2605.30637v1 Announce Type: new Abstract: Clinical decision-making (CDM) is central to real-world clinical workflows, where clinicians infer diagnoses, select treatments, or anticipate future health outcomes under incomplete evidence. LLMs are increasingly used to support these decisions due to strong language capabilities, broad biomedical knowledge, and efficiency, yet the reliability of LLMs on real-world clinical decision tasks remains insufficiently understood. To evaluate CDM models, especially LLM-based models, an ideal and practical medical decision benchmark should be constructed via an automated yet reliable pipeline to ensure both scale and quality. Moreover, the grounding of a CDM benchmark in real patient EHRs can better support evaluation on practical CDM tasks that require substantive biomedical knowledge and clinical inference. To fill the gaps, we introduce EHRBench, an automated and reliable EHR-grounded benchmark for evaluating LLM-based clinical decision-making at scale. To ensure scalability and reliability, EHRBench is constructed through an EHR-LLM-KB(knowledge-base) interaction pipeline. For efficiency, we use a specialized LLM to automatically convert encounter-level EHR trajectories into structured templates and deterministically instantiate the templates into QA items. In parallel, we apply systematic KB-based verification and enrichment to filter hallucinated or ambiguous relations and to improve reliability. Using this pipeline, we construct nearly 1M (960,067) QA items spanning three core inference-required clinical decision tasks: diagnosis, treatment, and prognosis. We benchmark more than 30 representative LLMs on EHRBench and provide detailed analyses of performance and robustness. The results show consistent capability trends across settings, further validating the reliability of EHRBench and highlighting actionable gaps toward clinically reliable LLM systems.
AI for Interoperability in Health Care: Philips’s Carla Goulart Peron
In this episode of the Me, Myself, and AI podcast, Philips’s chief medical officer Carla Goulart Peron shares how artificial intelligence is reshaping health care — not by replacing clinicians but by expanding access, improving diagnostics, and freeing doctors to focus more time on patients. Drawing on her experience practicing medicine in Brazil’s strained public […]
Healthcare Mechanisms from Policy-as-Code Search under Strategic Provider Response
arXiv:2605.30680v1 Announce Type: new Abstract: Healthcare mechanisms are inseparable from the strategic provider response they induce: existing healthcare AI benchmarks hold this response fixed and so cannot evaluate mechanisms by the equilibrium they produce. We recast hospital mechanism design as program synthesis for language models: typed, inspectable rule programs are executed and scored by
CVS Health Ventures Leads $40M Investment in H1 AI - DistilINFO Publications
CVS Health Ventures led a $40 million investment in H1 on May 28, 2026, following a successful collaboration that produced an AI model improving provider directory accuracy, with H1's platform serving 85% of the top 20 pharma companies and nine out of ten leading health plans through its Doctor ...
AI chatbots fail medical misinformation test, returning inaccurate and fabricated advice
A new study found that nearly half of the medical advice generated by popular AI chatbots like ChatGPT and Grok is problematic. The chatbots frequently provided incorrect health information, faked scientific references, and refused to admit ignorance.
The future of AI healthcare lies in a solid infrastructure backbone - IT-Online
In most walks of life, AI’s presence can already be felt. In healthcare, the benefits are, quite frankly, mindboggling; AI-powered platforms are unlocking new levels of efficiency and precision across medical practices. By Steven Santini, vice-president for secure power: SSA at Schneider ...
Council Post: Why Central AI Governance Committees Are Failing Healthcare—And Their Fix
If health systems, payers and pharma companies want to move from dozens of AI pilots to hundreds of production systems, the manual committee model has to change.
The Download: China’s brain implant ambitions
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. China has approved the world’s first invasive brain-computer chip—here’s what’s next Sitting in the courtyard of his house in China’s Henan province last October, Dong Hui decided to try holding a…
Overcoming Skepticism and Driving AI Adoption in Nursing - Emerj Artificial Intelligence Research
Nursing documentation has become an operational bottleneck that AI cannot fix without deep workflow alignment and disciplined change‑management. Nurses now spend up to 41% of their time on EHRs, according to the U.S. Department of Health and Human Services, and validated stress‑monitoring ...
Five Ways AI Is Transforming Cancer Care—and Companies That Are Making It Happen | AJMC
AI is poised to transform oncology with innovative tools enhancing diagnosis, treatment, and clinical trials, despite some wariness from clinicians and patients.
Council Post: AI In Healthcare 2026: The System May Be Broken. Let’s Try To Fix It
Healthcare fails in coordination, not capability. Yet most innovation still targets the wrong layer.
Digital Transformation in Healthcare Market to Hit USD 340 Billion by 2035 Amid AI and Telehealth Expansion – SNS Insider
U.S. market projected to hit USD 99.5 Billion by 2035, while Europe is forecast to reach USD 87.42 Billion as AI-driven clinical workflows and...
Frontier LLM-based agents can overcome the ontology curation bottleneck for natural phenotypes
arXiv:2605.28965v1 Announce Type: new Abstract: Linking free-text phenotype descriptions to ontology terms, typically referred to as phenotype annotation, is essential for the cross-study integration of comparative morphological data. This labor intensive process has heavily relied on highly trained human experts, which makes it challenging to scale and thus a key bottleneck. Dahdul et al. (2018) established a Gold Standard (GS) of Entity-Quality (EQ) annotations across seven phylogenetic studies and used it to evaluate three human curators and the Semantic CharaParser NLP tool with ontology-based semantic similarity metrics; they reported that machine-human consistency was significantly lower than inter-curator (human-human) consistency. Here we revisit that benchmark with five frontier hosted LLMs from Anthropic and OpenAI, each operating as an "agentic curator" within a self-contained workspace that supplies the source publication PDF, the same annotation guide used by the original human curators, the four project ontologies (UBERON, PATO, BSPO, GO), and a validation script. Evaluated against the same Gold Standard, every agent fell within the range of inter-curator variability of the three trained human biocurators of the original study; the best performing agents approached but did not reach the best performing human curator. Agents substantially outperformed Semantic CharaParser on all four metrics.
Compute Reality of Artificial Intelligence in Global Health LMICs - ICTworks
AI is not just a model. AI is compute, cloud, chips, data centers, energy, procurement power, cybersecurity, and governance.
K Health and Penn Medicine Partner to Launch Enterprise-Wide Clinical AI Architecture
Penn Medicine enters a multi-year collaboration with K Health to deploy clinical AI agents across its EHR, automating patient intake and reducing wait times.
Council Post: The Hidden Layer Every Healthcare AI Solution Is Missing
In the next wave of healthcare AI, differentiation will turn less on model sophistication and more on the quality and structure of the clinical knowledge beneath it.
AI Prognosis: Where patients and hospitals disagree about AI | STAT
In this edition of AI Prognosis, Brittany Trang takes a look at patients' role in how Stanford Health Care adopts AI tools, and more health AI news.
YC-backed French preventive health platform Lucis raises €17.3 million Series A led by Singular
Lucis, a Paris-based preventive health platform that uses blood biomarker analysis and AI to deliver personalised, science-based health recommendations, has raised €17.1 million ($20 million) in Series A funding. The round was led by Singular, with participation from General Catalyst, Y Combinator, and angels including investors behind Runna, Céline Lazorthes (Resilience), and Manu Lecomte. This […]
Authority Signals in Claude AI Health Citations: A Descriptive Analysis Using the Authority Signals Framework
arXiv:2605.23921v1 Announce Type: new Abstract: This study seeks to determine the authority signals used by Anthropic's Claude AI in its presentation of sources when answering consumer health questions. While there exists a great deal of discourse around the quality of health citations that LLMs produce, there is limited information on the integrity of the sources the citations originate from, and to what extent the sources are, from what health professionals would consider, credible sources. This descriptive cross-sectional study used data from HealthSearchQA, which contains 3,172 consumer health questions curated by Google Research. After exclusions, a final dataset of 3,075 questions yielding 10,038 citations was analyzed. The Authority Signals Framework (Jacques et al., 2026) was applied to examine 10 authority signals across four domains for a disproportionate stratified sample of 542 sources. Established institutional sources accounted for 97.8% of all citations (n = 9,818). Medical Institutions were the most frequently cited organization type (36.5%), followed by Government Resources (31.6%) and Professional Associations (28.4%). Commercial Health Information comprised 2.2% (n = 220). The top 10 organizations accounted for 57.8% of all citations, with Mayo Clinic alone representing 24.7%. Among commercial sources in the focused sample, 86.4% displayed medical review statements, 82.5% used schema markup, and 71.8% had comprehensive content, while traditional institutional sources appeared in Claude's citations with or without these same markers. As Anthropic positions Claude for HIPAA-ready healthcare applications, these findings establish a baseline for Claude's citation behavior and demonstrate the utility of the Authority Signals Framework as a tool for ongoing, cross-platform evaluation of AI-mediated health information.
When Correct Beliefs Collapse: Epistemic Resilience of LLMs under Clinical Pressure
arXiv:2605.23932v1 Announce Type: new Abstract: Despite strong medical benchmark accuracy, LLMs can exhibit severe multi-turn sycophancy in clinical dialogue, abandoning initial correct diagnosis under escalating pressure. We propose \textbf{\textsc{Med-Stress}}, a targeted stress test framework that evaluates belief stability under escalating pressure. Across nine frontier large language models (LLMs), we find a clear dissociation between medical knowledge and robustness: high initial diagnostic capability does not imply high belief stability, yielding large knowledge-robustness gaps for several LLMs. To mitigate this failure mode, we propose a lightweight inference-time defense, \textbf{\texttt{RBED}} (\textbf{R}ole-\textbf{B}ased \textbf{E}pistemic \textbf{D}efense), and \textbf{\texttt{R-FT}} (\textbf{R}esilience-oriented \textbf{F}ine-\textbf{T}uning), a training-time approach that internalizes evidence-based resistance to pressure. Experiments show that \textbf{\texttt{R-FT}} nearly eliminates belief change and substantially improves robustness.
AI Health Check: No Governance, No Trust - MedCity News
Will doctors or patients who are burned by one AI solution trust the next one they’re given? Probably not. That’s why every provider rolling out AI tools has to understand this risk and build governance into its development process.
New AI assistant streamlines initial psychiatric consultations for doctors
People often say that seeking psychiatric care can feel intimidating. Patients may feel burdened when they first open up about their emotional distress, while medical staff must accurately understand a patient's extensive history and symptoms within limited consultation time.
What Medicine Taught Us About Fairness and What It Missed: Lessons from Reconsidering Race-Specific Lung Function Reference Algorithms
arXiv:2605.24149v1 Announce Type: new Abstract: Since 2019, medical societies have reconsidered race-specific clinical equations often in parallel to and largely independent from algorithmic fairness research. Focusing on lung function reference algorithms that affect medical care, insurance, and employment for hundreds of millions globally, we analyze the transition from race-specific GLI-2012 to race-averaged GLI-Global through a fairness lens. Drawing on historical context, citation analysis, and quantitative evaluation, we show (i) limited cross-citation between FAccT and clinical guideline revision efforts; (ii) that GLI-Global implicitly encodes assumptions about social determinants of health, behaving as if ~62% of the Black-White gap in FEV1 is exposure-related; and (iii) clinical validation studies operationalized a sufficiency-like fairness criterion long before its formalization in fairness literature, while neglecting foundational results such as the impossibility theorem has led to inefficiencies in clinical research. Overall, our analysis highlights the value of deeper, mutually beneficial engagement between medical and fairness communities and the public to accelerate progress toward equitable healthcare algorithms.
Apple Watchに変革を、Whoopやオーラ台頭でヘルスケアアプリに課題－Power On
健康ウエアラブル端末市場で競争激化、AI時代への対応課題に
Iy\`aw\'oBench: A Benchmark for Evaluating Large Language Model Clinical Triage Accuracy on Undifferentiated Febrile Illness in Nigerian Primary Health Settings
arXiv:2605.23465v1 Announce Type: new Abstract: Background. Undifferentiated febrile illness is the leading cause of primary care outpatient visits in Nigeria, yet no validated benchmark exists for evaluating large language model (LLM) clinical triage reasoning in West African primary health settings. Methods. We introduce Iy\`aw\'oBench v1.0, a dataset of 200 synthetic clinical vignettes across eight febrile illness categories derived from statistical distributions of 1,200 real patient encounters at 19 primary health centres (PHCs) in Oyo State, Nigeria. Six LLMs were evaluated on structured triage classification across two metrics: triage accuracy and safety score. Results. All six models achieved 100% safety scores (95% CI: 96.4-100.0%), never downgrading a critical REFER NOW case to TREAT HERE. Triage accuracy varied substantially: Claude Sonnet (claude-sonnet-4-5) 67.5% (95% CI: 60.8-73.7%), Llama 4 Scout 59.5% (52.5-66.2%), Llama 3.3 70B 43.0% (36.2-50.0%), and Llama 3.1 8B 39.0% (32.4-45.9%). Two models demonstrated near-zero accuracy attributable to structured output non-compliance. Conclusions. Modern LLMs exhibit safe triage behaviour but vary substantially in structured clinical accuracy. Clinically engineered systems with embedded WHO guidelines outperform general-purpose models by up to 28.5 percentage points. Iy\`aw\'oBench provides the first reproducible evaluation framework for LLM clinical decision support in West African primary care.
Irish AI health-tech xWave to create 30 jobs amid €3m funding drive
xWave Technologies has earned more than 20 NHS Trusts contracts in the UK for its diagnostic decision-making platform. Read more: Irish AI health-tech xWave to create 30 jobs amid €3m funding drive
Opportunities and Risks of Generative AI through the Health Information Journey
arXiv:2605.23026v1 Announce Type: new Abstract: Artificial intelligence is fundamentally changing how health content is encountered and acted upon across both the information and healthcare ecosystems. AI systems now generate claims, curate information, interpret symptoms, synthesize evidence, and guide decisions, with significant opportunities and risks for the public. Potential benefits include improvements in access, comprehension, and continuity of care. At the same time, AI can introduce inaccurate or manipulative content that is difficult to distinguish from reliable guidance, and encourage automated decisions that affect care with little transparency or recourse. We introduce a four-stage framework to examine how these opportunities and risks unfold as the public moves through the information environment and into formal healthcare.
Engagement-Optimized Care: When LLMs become Mental Health Infrastructure
arXiv:2605.23787v1 Announce Type: new Abstract: General-purpose LLMs are increasingly functioning as mental health infrastructure due to gaps in care left by provider shortages, inadequate insurance coverage, social isolation, and stigma around formal help-seeking. This shift poses a distinct problem for AI ethics: systems neither designed nor governed as care technologies are being used as such, while their dominant design incentives optimize for engagement rather than user well-being. We present findings from a qualitative, longitudinal study with 18 US-based participants who use general-purpose LLMs for socioemotional support and participated in one or more of our study phases, including initial interviews, a four-week diary study, focus groups, and exit interviews. Participants turned to LLMs because other forms of support were unavailable, unaffordable, socially costly, or inadequate. As they continued to use these systems, design features such as anthropomorphic cues, default validation, persistent responsiveness, and weak disengagement mechanisms shaped their ongoing reliance. Participants described meaningful support alongside dependency, epistemic distortion through one-sided validation, privacy expectations without corresponding legal protection, and continued use despite awareness of these risks. We argue these dynamics reflect a structurally unfair tradeoff: users accept risks because support is otherwise absent, while available systems are optimized to deepen engagement and lack care-based accountability. The paper makes three contributions: it traces the arc through which LLMs become care infrastructure and identifies distinct ethical tensions at each stage, shifts analysis from turn-based exchanges to longitudinal trajectories of use, and argues that accountability belongs at the design and incentive conditions through which these systems become care infrastructure rather than at the output or crisis-response layer.
Finland’s Grundium acquires Denmark’s Visiopharm to build an end-to-end AI precision pathology platform
Grundium, a Tampere-based startup specialising in digital pathology imaging technology, backed by US-based healthcare private equity firm EW Healthcare Partners, has acquired Visiopharm, a Denmark-based provider of AI-driven precision pathology software. The combined business merges complementary capabilities from Grundium’s imaging platform and Visiopharm’s AI-driven precision pathology software, creating an accessible end-to-end solution for diagnostic laboratories, […]
How AI could help fix Kenya's overstretched healthcare system - The Standard Health
Kenya continues to face growing demand for healthcare services alongside persistent shortages of healthcare personnel, particularly in specialised areas of care.
‘You can’t control everything’: the rise in plastic surgeons asked to create ‘AI face’
Growing numbers of people are seeking improbable cosmetic surgery based on chatbots’ recommendations Plastic surgeons are increasingly concerned about the rise of “AI face”, as more and more clients arrive in their offices with unrealistic AI-generated visions of what they want to look like. Dr Nora Nugent, a cosmetic surgeon from Tunbridge Wells, has seen this first hand. Clients have started coming to her office with photos of themselves beautified by AI and a false expectation that those results are achievable with surgery. She is also the president of the British Association of Aesthetic Plastic Surgeons, and says many colleagues are having similar experiences. Continue reading...
Healthcare LLM Benchmarks Are Only as Good as Their Explicit Assumptions
arXiv:2605.22612v1 Announce Type: new Abstract: Benchmarks are necessary for healthcare evaluation, but are not sufficient for predicting deployment performance. Our position is that the evaluation--deployment gap arises not because of poorly designed benchmarks, but from implicit assumptions about how users interact with models that cannot be surfaced from benchmarks alone. To make this precise, we propose a classification of assumptions into two categories: task, which can be tested from conversation data alone, and outcome, which requires outcome data and behavioral studies for testing. Critically, outcome assumptions depend on human behavior, something that even well-designed benchmarks cannot directly observe. To demonstrate the operationality of this framework, we retrospectively analyze a healthcare RCT as a case study and find that the gap naturally separates into task and outcome gaps of roughly equal size. To address this, we make two contributions: first, we propose BenchmarkCards, an artifact that documents assumptions, and second, we propose staged evaluation, a procedure that systematically tests assumptions and evaluates performance.
AI-powered diagnostics: What will the technology look like in 5-10 years? | Medical Economics
AI tools are becoming more prevalent in back office operations, but they are also making inroads on the diagnostic side.
Privacy-by-Design Adaptive Group Assignment for Digital Lifestyle Coaching at Scale
arXiv:2605.20505v1 Announce Type: cross Abstract: Digital lifestyle coaching systems must personalize peer support as user behavior and engagement evolve while preventing personally identifiable information (PII) and sensitive health information from leaking into analytics and AI pipelines. This creates a practical tension: personalization requires longitudinal linkability, while privacy engineering requires minimization, separation, and controlled re-identification. We present PRISM-Coach, a stakeholder-centered architecture and adaptive peer-group assignment method for privacy-preserving lifestyle coaching. PRISM-Coach separates each user into four bounded views: Identity, Operational, Learning, and Coaching, each with distinct access controls and risk profiles. Building on this separation, the system uses vault-based controlled identity restoration, a privacy-constrained contextual bandit to assign users to eligible peer groups under coach-capacity and stability constraints, and a human-in-the-loop coaching assistant that generates de-identified summaries and draft messages without sending raw PII or PHI to external AI services. We instantiate PRISM-Coach in a commercially deployed lifestyle coaching platform and evaluate it using three years of telemetry from approximately 2,800 users and an in-app needs assessment survey. At the population level, daily check-in adherence increases from 0.35 to 0.68, and engagement rises to 1.35 baseline. In a matched 19-week comparison window, the AI-enabled workflow achieves adherence of 0.74 versus 0.48 under static grouping and higher average weight loss: 5.2 kg versus 3.1 kg. Survey results show that 82% report positive perceived benefit, and 92% report increased privacy confidence after transparency disclosures. These results position PRISM-Coach as a practical blueprint for privacy-by-design adaptive learning systems in everyday wellness.
Do No Harm? Hallucination and Actor-Level Abuse in Web-Deployed Medical Large Language Models
arXiv:2605.20591v1 Announce Type: cross Abstract: Medical large language models (LLMs), including custom medical GPTs (MedGPTs) and open-source models, are increasingly deployed on web platforms to provide clinical guidance. However, they pose risks of hallucination, policy noncompliance, and unsafe design. We conduct a large-scale assessment of 6,233 MedGPTs, evaluating a stratified sample of 1,500, together with 10 open-source LLMs. We introduce two frameworks: MedGPT-HEval for hallucination detection and an LLM-based pipeline for assessing policy violations and developer intent. Our results show that 25-30% of MedGPTs exhibit low factual accuracy, with bottom- and middle-tier models at highest risk; 33.6-54.3% violate operational thresholds, and 57.06% of Action-enabled models lack adequate privacy disclosures. Compared with open-source models, MedGPTs achieve higher factual accuracy and semantic alignment, though open-source models are more stable. These results reveal systemic gaps in hallucination and compliance, highlighting the need for multi-metric evaluation and stronger safeguards. We release HAA-MedGPT, a structured dataset that supports future research on the safety of web-facing medical LLMs.
Artificial Pancreas Implantables -- How Healthcare Professionals May Deal With DIY Bio Cases
arXiv:2605.20208v1 Announce Type: cross Abstract: Automated insulin delivery (AID) and artificial pancreas systems increasingly serve as safety-critical cyber-physical technologies in clinical care, integrating sensors, algorithms, software, and insulin-delivery hardware to automate a life-sustaining therapy. While regulated commercial systems are supported by formal approval pathways, manufacturer governance, and post-market surveillance, clinicians are also encountering patients who rely on do-it-yourself (DIY) artificial pancreas systems that operate outside conventional regulatory and institutional control structures. This paper examines how routine clinical handling practices intersect with cyberbiosecurity risk across both regulated and DIY AID systems. When insulin delivery systems are fundamentally reconfigured into a bespoke AID system, with the patient-user becoming the primary threat vector by assuming manufacturer-level roles without mandated governance, the entire ecosystem of stakeholders is placed in legal and clinical uncertainty.
60% of Healthcare Firms Use AI for Chatbots | PYMNTS.com
Healthcare’s AI adoption is narrower than other sectors, but the industry is using it where operational strain is most immediate.
The Missing Link in Healthcare AI Adoption: Workforce Readiness | Healthcare IT Today
The following is a guest article by Anupama Shashank, Managing Director & Senior Vice President, Healthcare & Life Sciences at Kyndryl Nearly all healthcare organizations are deploying AI across clinical, operational, and administrative functions, outpacing the global average.
Singapore unveils healthcare AI deals on diabetes, dementia and Bhutan
Singapore announced new cross-border healthcare AI partnerships to support disease detection and diagnostic models in both local and rural Bhutanese hospitals.
XCHANGE ‘26 Attendee Insights Highlight Healthcare AI’s Shift From Adoption to Operational Scale
Hospital leaders attending Xsolis’ XCHANGE ‘26 user conference signaled a broader shift in how orgs are approaching AI, moving from adoption to scale....
Corti's new Symphony for Speech-to-Text model beats OpenAI at medical terminology accuracy, highlighting the value of specialized AI
Today, Copenhagen-based healthcare AI Corti is launching Symphony for Speech-to-Text, a new generation of clinical-grade speech recognition models engineered specifically for real-time dictation, conversational transcription, and batch audio processing — and their accuracy rate is the highest for this specific use case yet recorded. "We are focused on ensuring our AI scribes can be trusted by physicians, medical practitioners and patients...the entire healthcare system," said Andreas Cleve, co-founder and CEO of Corti, in an exclusive video call interview with VentureBeat. The performance data the company is bringing to the table paints a stark picture of the current state of enterprise AI: when it comes to highly regulated, specialized industries, domain-specific models can beat out the foundation model providers. In a newly published research paper, Corti revealed that its new clinical-grade speech models reduced word error rates (WER) by up to 93% when compared against leading generalist speech models and APIs on medical terminology. On English medical terminology, its Symphony for Speech-to-Text achieved a remarkably low 1.4% WER. By comparison, OpenAI’s speech model registered a 17.7% WER, ElevenLabs hit 18.1%, Whisper recorded 17.4%, and Parakeet scored 18.9%. Corti’s announcement serves as a critical inflection point for healthcare builders. While general-purpose APIs like OpenAI’s whisper are sufficient for broad-domain transcription, they frequently stumble over medical acronyms, complex medication dosages, shorthand, and noisy emergency room environments. Symphony for Speech-to-Text aims to solve this by providing developers with a highly specialized, production-grade API designed from the ground up for clinical workflows. The agentic era demands flawless data inputs The launch of Symphony for Speech-to-Text highlights a fundamental shift in how healthcare uses voice technology. For decades, medical speech recognition was primarily about generating a static text document for human doctors to review—a digital replacement for a notepad. But as the healthcare industry hurtles into what technologists call the "agentic era," where autonomous AI agents actively assist in clinical decision-making, EHR navigation, and real-time support, the transcript is no longer the final product. It is the foundational data layer. “Speech has always been one of healthcare’s most important inputs,” Cleve said in a statement provided to VentureBeat. “What is changing is what happens after the words are captured. In the agentic era, speech recognition requires more than simply producing a transcript - we need to give AI systems accurate clinical facts to reason from. If a model mishears a medication, dosage, or symptom, every downstream step becomes less reliable. Symphony for Speech-to-Text gives healthcare builders a speech layer accurate enough to thrive in clinical reality.” This is where the compounding danger of high word error rates comes into play. If a general-purpose AI model hallucinates a transcription—turning "hyperthyroidism" into "hypothyroidism," or misinterpreting a critical medication dosage—every subsequent AI agent relying on that transcript will operate on corrupted data. Corti’s architecture mitigates this risk by producing structured, clinically usable output directly from the API, helping downstream AI applications reason over clean facts rather than messy, unformatted text. Nowhere is this more evident than in Corti’s entity recall benchmarks. Symphony for Speech-to-Text reached an astonishing 98.3% recall rate on formatted clinical entities—such as dosages, measurements, and dates. In contrast, Corti reported that the strongest general-purpose baseline model maxed out at just 44.3% recall for the same entities. For developers building ambient AI documentation tools, that 54% gap is the difference between a tool that saves a physician time and a tool that constitutes a medical liability. Dethroning the industry ldears While Corti’s benchmarks against modern LLM builders like OpenAI and ElevenLabs are striking, the company is also taking aim at legacy medical transcription giants. For years, the gold standard for dedicated clinician dictation has been Dragon Medical One. However, these legacy systems were historically optimized strictly for intentional clinician dictation, not as underlying infrastructure for ambient AI, complex multi-party conversations, or real-time clinical support tools. In evaluations of real-world English medical dictation, Corti achieved a 4.6% WER, outperforming Dragon’s 5.7% (a 19% relative improvement). Furthermore, Corti demonstrated a higher medical term recall than Dragon (93.5% versus 92.9%). By providing this level of accuracy via an API endpoint, Corti is enabling third-party developers, EHR vendors, and virtual care platforms to build their own custom dictation and ambient listening tools that outperform the industry's legacy incumbent. "We want people to build apps atop our models," Cleve said. "The goal is to diffuse the technology as widely as it is needed so it can be as helpful as possible to patients and their doctors and professionals." For Cleve and his co-founders, the mission is a personal one: Cleve's own mother was a healthcare professional attacked by a patient and spent years struggling to recover. He sought to improve healthcare processes as a way of honoring her sacrifice. Solving the healthcare model puzzle The demands of healthcare extend far beyond English-speaking hospitals, and global health systems have historically been underserved by clinical NLP models. Early adopters are already leveraging Corti’s new models in linguistically demanding environments, proving the technology's viability in complex international markets. Switzerland, for instance, requires care delivery across multiple languages—often simultaneously within a single medical institution. It serves as one of the most stringent proving grounds for multilingual medical speech models in the world. Corti’s Symphony models demonstrated massive performance gains in these non-English tests, achieving a 2.4% WER in German (compared to 13.0% for the next-best system) and a 3.9% WER in French (versus 10.6%). “In a clinical conversation, every word matters - a missed medication name, a misheard dosage, or a mistranscribed symptom can change the meaning of an encounter," said Pierre Corboz, Head of Solutions & Business Development at Voicepoint, a Swiss healthcare technology provider, in a statement provided to VentureBeat. "Symphony’s accuracy on clinical terminology gives us the foundation to bring more trusted AI capabilities into clinical workflows with our Voicepoint Xenon platform. When Corti improves the speech layer, the workflows we build together become sharper, safer, and more useful for clinicians in Switzerland.” AI vrticalization and specialization are yielding gains Today’s announcement of Symphony for Speech-to-Text is not an isolated event; it is the culmination of a strategic narrative Corti has been aggressively pushing over the last several weeks. The broader Symphony platform—which powers clinical and administrative applications for a global network of EHR vendors and life sciences organizations—has been systematically proving the defensibility of vertical AI labs against horizontal tech giants. This marks the third major benchmark Corti has released in just six weeks, touching different layers of healthcare AI performance. In April, the company revealed that its Symphony for Medical Coding system outperformed general-purpose models by more than 25% in clinical accuracy benchmarks, tackling one of healthcare’s most notoriously complex workflows. And just last week, Corti announced that its flagship clinical-grade model outscored OpenAI on HealthBench Professional, OpenAI’s own healthcare benchmark. Taken together, these three data points—medical coding, clinical reasoning, and speech-to-text accuracy—illustrate a growing consensus in the enterprise technology sector: generalized models are hitting a ceiling in regulated industries. Models deployed in hospitals must inherently understand complex acronyms, sudden interruptions, medical shorthand, specialty-specific language, and strict compliance constraints. By training specifically on these unique edge cases, vertical AI labs like Corti are building a formidable moat that companies relying solely on API calls to generalized large language models cannot easily cross. Availability and product lineup Developers are clearly taking notice of the performance gap. According to momentum data provided to VentureBeat, Corti is seeing a 30% growth in new sign-ups for its platform in quarter-to-date comparisons, signaling that developers and healthcare builders are actively gravitating toward vertical, clinical-grade models over generalist APIs. Corti, which already serves over 100 million patients annually across major health systems including the UK’s National Health Service (NHS), is positioning Symphony for Speech-to-Text as the default engine for the next generation of healthcare software. It is important to note that Corti is not launching the overarching Symphony platform itself today; rather, Symphony for Speech-to-Text operates as a new, distinct capability within that broader ecosystem, accessible via its own API endpoints. Symphony for Speech-to-Text is generally available starting today. Developers and enterprise architects can access the models via the Corti API console, with full technical documentation available to help integrate the clinical-grade speech layer into their existing applications. In a move toward research transparency, Corti has also published its full research paper detailing its methodology, along with a separate comparison tool designed to support transparent evaluation of medical speech recognition systems across the industry. As the healthcare industry continues its rapid embrace of AI-driven automation, the foundational data layer has never been more critical. Corti’s latest launch is a stark reminder that in the medical field, generic AI simply isn't good enough. The future belongs to the specialists.
Evaluating the Utility of Personal Health Records in Personalized Health AI
arXiv:2605.18937v1 Announce Type: new Abstract: Patient-managed Personal Health Records (PHRs) promises to empower patients to better understand their health; but information in the record is complex, potentially hindering insights. In this study, we assess the potential of large language models (LLMs, Gemini 3.0 Flash) to provide helpful answers to user health queries, when provided clinical data from PHRs as context. A total of 2,257 user queries were drawn from 3 different distributions to represent patient questions: shorter web search queries, longer questions derived from templates of chatbot conversations, and questions patients asked to their healthcare team (patient calls). Queries were matched with de-identified PHRs (from a pool of 1,945). Gemini responses were generated (1) without PHR context; (2) with a basic summary of demographics, conditions, and medications; (3) with full, extensive clinical notes. For evaluation, we leveraged an existing rating framework (SHARP), and developed a new framework for specific error modes when interpreting PHRs. Evaluation was performed using autoraters for the full set, and with clinician ratings for a subset (n=95), with both sets of raters knowing the full PHR context. We see significant improvements in the helpfulness of answers to all question types with PHR data (p < 0.001, paired t-test). We also observe potential gains in safety, accuracy, relevance and personalization of answers. Our PHR evaluation framework further identifies gaps in LLM understanding of particular aspects of complex PHRs, such as temporal disorientation, and rare but meaningful confabulations. These results suggest potential for PHR data to help people with a wide range of user needs; and provide a framework for monitoring for gaps in LLM answers based on PHR context. This study motivates further work to assess and realize potential benefits to users from understanding their health records.
Big Europe and Asian private equity health funds merge to defy AI disruption
Global Healthcare Opportunities and CBC Group say $21bn investment manager will be world’s largest in sector
Comment: What does successful AI adoption look like? - Healthcare Today
Roy Wills, global head of healthcare business and partnerships at Intellias, argues that healthcare’s AI problem is not innovation, it’s implementation.
AI preparedness gap hits frontline industries hardest in 2026 - Outsource Accelerator
Hospitality, healthcare and logistics rank among the industries least prepared for AI workforce disruption in 2026, according to a new analysis.
Generative AI and Two-Tiered Online Mental Health Communities
arXiv:2605.16279v1 Announce Type: new Abstract: Online mental health communities (OMHCs) are tiered platforms that connect patients with licensed counselors through public Q&A forums and paid private consultations. Their two-tier structure creates a strategic dilemma for genAI integration. Conversational agents can provide scalable and timely responses to a broader set of patients, alleviating persistent supply shortages, but their large-scale presence may also reshape counselors' participation in providing nuanced expertise, emotionally sensitive support, and paid consultations, which are central to platform revenue and long-run sustainability. Leveraging a quasi-natural experiment from the integration of a genAI-based conversational agent in a leading OMHC, we examine how AI entry affects counselor participation. Using multiple identification strategies, we find that posting intensity increases significantly after AI integration, while average response length remains unchanged and per-post social recognition declines. Mechanism analyses show that AI improves responsiveness and expands patient engagement, enlarging counselors' opportunity sets, with activity partially reallocated from a nearby non-AI subforum. Counselors respond heterogeneously: intrinsically motivated counselors reduce participation, whereas economically motivated counselors intensify competitive effort. These dynamics generate cross-tier spillovers: inactive counselors experience declines in paid consultations, while those who increase public participation preserve or expand downstream demand. Overall, our findings show that in tiered professional platforms, demand expansion and competitive incentives can outweigh intrinsic crowding-out.
When AI Tells You What You Want to Hear: Sycophantic Behavior of Large Language Models in Dementia Care Settings
arXiv:2605.16288v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used in clinical and care settings. This exploratory study investigates whether LLMs exhibit sycophantic behavior - adapting their responses to social expectation signals rather than maintaining professional quality - in the context of dementia care. Five prompts with systematically increasing confirmatory and authority-related framing (P1 neutral to P5 authority-signaled implementation support) were submitted to four LLMs (GPT-5, Claude Sonnet 4.6, Gemini 3.1 Pro, Mistral Large), each repeated five times (N = 100 responses). Responses were evaluated using an LLM-as-a-Judge methodology against seven nursing-ethical quality criteria (K1-K7) and a tone scale (0-3). All models showed significant negative Spearman correlations between prompt level and response quality (rho ranging from -0.543 to -0.734, all p < 0.01). Mistral Large exhibited the most pronounced effect (rho = -0.734), with mean scores dropping from 6.0/7 at P1 to 0.2/7 at P5. The findings suggest that LLMs pose context-sensitive risks in high-stakes care environments and that prompt framing significantly shapes response quality - a dimension that has received insufficient attention in healthcare AI deployment.
Healthcare AI firm Commure valued at $7 billion, raises $70 million | Reuters
Agentic AI — which can plan, decide and act autonomously rather than just respond to prompts — has become one of venture capital's most sought-after areas, as investors pile into businesses using the technology to streamline operations.
Payer-Led AI Adoption Emerges as Key Theme in Healthcare Technology - TipRanks.com
According to a recent LinkedIn post from Ember, a conversation with Dr. Kevin Stevenson characterizes health care payers as the leading force in AI and technology i...
Council Post: Winning With AI In Healthcare Starts With Choosing The Right Workflows
Simply, the best clinical AI strategies aren’t focused on technology but workflows.
Council Post: Transforming Healthcare Payer Operations With AI Building Blocks
If early AI in healthcare was defined by experimentation, I believe the next phase will be defined by architecture.
Implement AI in the mid-cycle of rev cycle for the biggest return | Healthcare Finance News
ROI shows fairly quickly, and tools can be used right away to advance from simple to more complex cases, says Jeff Francis, CFO and VP for the Methodist Health System.
What Health Care Leaders Have Learned From Deploying AI | AJMC
Experts examine what is working in production, where the guardrails are being tested, and why the most transformative chapter of AI in health care hasn’t started yet.
AI 'Industrial Revolution' Taking Place, Says Boston Children's Chief Medical Officer
Dr. Joan LaRovere, chief medical officer at Boston Children’s Hospital, said that AI can 'bolster' the capabilities of health care providers to help diagnose and treat patients more effectively. Dr. LaRovere said that she believes the moment in time is like the 'industrial revolution' that can revolutionize health care. (Source: Bloomberg)
National AI Doctors Mission Launched In New Delhi: Here Is How It Will Change Indian Healthcare
The National AI Doctors Mission launches in New Delhi. Here is how it will change the Indian healthcare landscape and the challenges that may arise.
Melbourne psychiatrist refuses new patients who don’t consent to AI note-taking
Registration form informs patients that if they do not wish AI to be used, they will need their referring doctor to refer them to a different service provider Get our breaking news email, free app or daily news podcast A Melbourne psychiatrist has refused new patients unless they agree to allow her to use an AI scribe to transcribe the conversations in their sessions. AI-driven note-taking tools are becoming popular within the medical industry – with two in five general practitioners now using such scribes, according to the Royal Australian College of General Practitioners (RACGP). Continue reading...
Q&A: Why pricey AI prototypes are often left on the cutting room floor | Healthcare IT News
Michael Privat, chief data and engineering officer of Availity, offers his perspective on what healthcare organizations need to scale artificial intelligence projects that last – and what it takes to achieve end-to-end AI observability.
UnitedHealth Tracks Workers’ AI Use in Push to Transform Company
UnitedHealth Group Inc. is tracking how often some employees use artificial intelligence tools as part of a push to embed the technology throughout its operations, according to people familiar with the matter.
AI, Robotics Key to Transforming Health Care: BD CEO
Tom Polen, CEO of medical technology company BD, says that in the next decade AI and robotics will transform health care in ways that will make today’s system seem archaic. Polen sits down with Bloomberg’s Caroline Hyde on the sidelines of the Consello Spark Summit. (Source: Bloomberg)
MiniMed Aims to Be 'Self-Driving Car' of Diabetes Care
Diabetes equipment provider MiniMed is aiming to be the “self-driving car” of insulin pumps, says its CEO, Que Dallara. Hot off the heels of the company’s IPO, Dallara speaks with Caroline Hyde on the sidelines of the Consello Spark Summit. (Source: Bloomberg)
Network-Aware Bilinear Tokenization for Brain Functional Connectivity Representation Learning
arXiv:2605.14048v1 Announce Type: new Abstract: Masked autoencoders (MAEs) have recently shown promise for self-supervised representation learning of resting-state brain functional connectivity (FC). However, a fundamental question remains unresolved: how should FC matrices be tokenized to align with the intrinsic modular organization of large-scale brain networks? Existing approaches typically adopt region-centric or graph-based schemes that treat FC as structurally homogeneous elements and overlook the large-scale network brain organization. We introduce NERVE (Network-Aware Representations of Brain Functional Connectivity via Bilinear Tokenization), a self-supervised learning framework that redefines FC tokenization by partitioning FC matrices into patches of intra- and inter-network connectivity blocks. Unlike image-based MAE, where fixed-size patches share a common tokenizer, FC patches defined by network pairs are heterogeneous in size and correspond to distinct functional roles. To resolve this problem, NERVE embeds FC patches through a novel structured bilinear factorization. This formulation preserves network identity and reduces parameter complexity from quadratic to linear scaling in the number of networks. We evaluate NERVE across three large-scale developmental cohorts (ABCD, PNC, and CCNP) for behavior and psychopathology prediction. Compared to structurally agnostic MAE variants and graph-based self-supervised baselines, the proposed network-aware formulation yields more stable and transferable representations, particularly in cross-cohort evaluation. Ablation studies confirm that the proposed bilinear network embedding and anatomically grounded parcellation are critical for performance. These findings highlight the importance of incorporating domain-specific structural priors into self-supervised learning for functional connectomics.
Healthcare AI Evaluation Frameworks: Moving Beyond Accuracy to Safety and Fairness
Until evaluation frameworks reflect the realities of the environments they are deployed in – workflow complexity, human behavior, data instability, and system risks – healthcare AI deployments will lack the reliability needed to truly deliver consistent clinical value and outcomes.
How AI Aims to Fix Healthcare Access
Rezilient CEO Dr. Danish Nagda says the healthcare system is at a tipping point. He joins Bloomberg Open Interest to talk about how hybrid “cloud clinics,” employer-driven care, and AI-powered doctors could eliminate long wait times, cut costs, and make switching doctors a thing of the past. (Source: Bloomberg)
Multimodal Hidden Markov Models for Persistent Emotional State Tracking
arXiv:2605.12838v1 Announce Type: new Abstract: Tracking an interpretable emotional arc of a conversation via the sentiment of individual utterances processed as a whole is central to both understanding and guiding communication in applied, especially clinical, conversational contexts. Existing approaches to emotion recognition operate at the utterance level, obscuring the persistent phases that characterize real conversational dynamics. We propose a lightweight framework that models conversational emotion as a sequence of latent emotional regimes using sticky factorial HDP-HMMs over multimodal valence-arousal representations derived from simultaneous video, audio and textual input. We evaluate the quality of regime prediction using LLM-as-a-Judge, geometric, and temporal consistency metrics, demonstrating that the sticky HDP-HMM produces more interpretable regime sequences than the baseline Gaussian HMM at a fraction of the computational cost of LLM-based dialogue state tracking methods. In addition, Question-Answer experiments in a clinical dataset suggest that meaningful emotional phases can reliably be recovered from multimodal valence-arousal trajectories and used to improve the quality of LLM responses in unstable affective regimes via context augmentation. This framework thus opens a path toward interpretable, lightweight, and actionable analysis of conversational emotion dynamics at scale.
WhatsApp Vaccine Discourse (WhaVax): An Expert-Annotated Dataset and Benchmark for Health Misinformation Detection
arXiv:2605.12510v1 Announce Type: cross Abstract: We introduce WhaVax, a new expert-annotated dataset of vaccine-related WhatsApp messages collected from large Brazilian public groups spanning multiple pandemic years. The dataset was constructed through a rigorous, carefully designed pipeline that integrates keyword-based data collection, semantic deduplication to remove near-duplicate content, and a multi-stage annotation protocol conducted by medical specialists. This process produced a high-quality gold-standard corpus, characterized by substantial inter-annotator agreement and strong reliability for downstream analysis. Additionally, we provide a detailed characterization of WhatsApp misinformation, revealing distinctive linguistic, structural, lexical, temporal, and group-level patterns, as well as a meaningful layer of ambiguous cases that reflect the complexity of health discourse in private messaging. We also benchmark classical models, fine-tuned Small Language Models, and zero- or few-shot Large Language Models under realistic data-scarcity constraints, demonstrating that strong embeddings and LLM approaches perform competitively, while domain alignment and data availability remain critical factors. This study provides a rare, high-quality resource to support misinformation research and computational modeling in encrypted communication environments.
Greater Manchester still says no to NHS data platform with Palantir at its heart
Public concern has only grown, says ICB, while evidence of benefits remains thin
OpenEvidence Exits Europe Over Regulatory Rules | Telehealth.org
OpenEvidence exits EU and the UK, highlighting tensions between AI regulation, innovation, and patient safety in digital health.
Half of AI Health Advice Is Wrong—And Seems Just Right - Decrypt
A peer-reviewed audit in BMJ Open found that nearly 50% of health responses from five major AI chatbots were problematic, with fabricated sources and confident delivery.
One in seven in UK prefer consulting AI chatbots to seeing doctor, study finds
Exclusive: Doctors say ‘highly concerning’ poll highlights risk to patients of turning to AI for medical advice One in seven people are using AI chatbots for health advice instead of seeing their GP, a UK study has found. The poll of more than 2,000 people found that – of the 15% turning to chatbots – one in four had done so because of long NHS waiting lists. Continue reading...
Measuring What Matters: Benchmarking Generative, Multimodal, and Agentic AI in Healthcare
arXiv:2605.08445v1 Announce Type: new Abstract: AI models are increasingly deployed in live clinical environments where they must perform reliably across complex, high-stakes workflows that standard training and validation datasets were never designed to capture. Evaluating these systems requires benchmarks: structured combinations of tasks, datasets, and metrics that enable reproducible, comparable measurement of what a model can do. The central challenge in healthcare AI is not performance alone, but the absence of systematic methods to measure reliability, safety, and clinical relevance under real-world conditions. Most existing benchmarks test what a model knows; too few test whether it can perform reliably and without failing across the full complexity of real clinical tasks. Current benchmarks have accumulated through ad hoc dataset construction optimized for narrow task performance: frontier models achieve near-perfect scores on medical licensing examinations, but when evaluated across real clinical tasks, performance degrades sharply, scoring 0.74--0.85 on documentation, 0.61--0.76 on clinical decision support, and only 0.53--0.63 on administrative and workflow tasks \cite{medhelm}. High benchmark scores give a false sense of deployment readiness, and the gap between performance and utility widens precisely as AI systems take on more consequential clinical roles. Without a principled framework for benchmark design, the field cannot determine whether poor clinical performance reflects model limitations or failures in how performance is being measured.
Rotterdam’s Ditto raises €7.6 million to make “what did the doctor say?” easier to answer
Ditto, a Rotterdam-based HealthTech startup that has developed a free app that translates complex medical information into plain language, has raised €7.6 million for its European rollout. The round was led by Heal Capital, with participation from Optiverder and Rubio Impact Ventures. “No patient should have to guess what was just said. We are fundamentally […]
MedThink: Enhancing Diagnostic Accuracy in Small Models via Teacher-Guided Reasoning Correction
arXiv:2605.08094v1 Announce Type: new Abstract: Accurate clinical diagnosis requires extensive domain knowledge and complex clinical reasoning capabilities. Although large language models (LLMs) hold great potential for clinical reasoning, their high computational and memory requirements limit their deployment in resource-constrained environments. Knowledge distillation (KD) can compress LLM capabilities into smaller models, but traditional KD merely transfers superficial answer patterns and fails to preserve the structured reasoning required for reliable diagnosis. To address this, we propose a two-stage distillation framework, MedThink, designed to cultivate robust clinical reasoning in small language models (SLMs). In the first stage, a teacher LLM screens data and injects domain-knowledge explanations to fine-tune a student model, establishing a knowledge foundation. In the second stage, the teacher evaluates the student's errors, generates reasoning chains linking knowledge to correct answers, and refines the student's diagnostic reasoning through a second round of fine-tuning. We evaluate MedThink on general medical benchmarks and a gastroenterology dataset comprising 955 question-answer pairs. Experiments demonstrate that MedThink outperforms six distillation strategies in all benchmarks: achieving an improvement of up to 12.7% over the student baseline in general tasks, and reaching a total top accuracy of 56.4% in gastroenterology evaluation. This indicates that iterative distillation centered on reasoning can significantly enhance the diagnostic accuracy and generalization capabilities of SLMs whilst maintaining computational efficiency. Our code and data are publicly available at https://github.com/destinybird/PrecisionBoost.
Palantir’s access to identifiable NHS England patient data is ‘dangerous’, MPs say
Health service has given US tech firm ‘unlimited access’ to certain data to build integrated platform, according to reports UK politics live – latest updates MPs have warned that an NHS decision to grant Palantir access to identifiable patient information in its plan to use AI to improve the health service is “dangerous” and will fuel public fears that data privacy is not being prioritised. NHS England has allowed staff from the US tech firm and other contractors to access patient data before it has been pseudonymised, despite internal fears of a “risk of loss of public confidence”, the Financial Times reported. Continue reading...
Secai Partners With Mila to Accelerate AI-Powered Healthcare Across North America | Markets Insider
MONTREAL, May 11, 2026 (GLOBE NEWSWIRE) -- Secai, the Montreal-based healthcare AI company behind the Voxira platform, is proud to announce a st...
New AI model spots pancreatic cancer up to 3 years earlier than human doctors in test
A new AI diagnostic model has demonstrated the ability to detect pancreatic cancer significantly earlier than traditional human screening.