Healthcare

New Gallup Poll Finds That Low-Income Americans Are Turning to AI as a Replacement for Expensive Doctor's Visits

Some report forgoing healthcare visits because of AI-generated advice. Fourteen percent of recent users say the AI information or advice they received led them to skip a provider visit in the past 30 days.

Toward Zero-Egress Psychiatric AI: On-Device LLM Deployment for Privacy-Preserving Mental Health Decision Support

Privacy represents one of the most critical yet underaddressed barriers to AI adoption in mental healthcare -- particularly in high-sensitivity operational environments such as military, correctional, and remote healthcare settings, where the risk of patient data exposure can deter help-seeking behavior entirely. Existing AI-enabled psychiatric decision support systems predominantly rely on cloud-based inference pipelines, requiring sensitive patient data to leave the device and traverse externa...

AI Approach for MRI-only Full-Spine Vertebral Segmentation and 3D Reconstruction in Paediatric Scoliosis

MRI is preferred over CT in paediatric imaging because it avoids ionising radiation, but its use in spine deformity assessment is largely limited by the lack of automated, high-resolution 3D bony reconstruction, which continues to rely on CT. MRI-based 3D reconstruction remains impractical due to manual workflows and the scarcity of labelled full-spine datasets. This study introduces an AI framework that enables fully automated thoracolumbar spine (T1-L5) segmentation and 3D reconstruction from ...

DeepER-Med: Advancing Deep Evidence-Based Research in Medicine Through Agentic AI

arXiv:2604.15456v1 Announce Type: new Abstract: Trustworthiness and transparency are essential for the clinical adoption of artificial intelligence (AI) in healthcare and biomedical research. Recent deep research systems aim to accelerate evidence-grounded scientific discovery by integrating AI agents with multi-hop information retrieval, reasoning, and synthesis. However, most existing systems lack explicit and inspectable criteria for evidence appraisal, creating a risk of compounding errors and making it difficult for researchers and clinicians to assess the reliability of their outputs. In parallel, current benchmarking approaches rarely evaluate performance on complex, real-world medical questions. Here, we introduce DeepER-Med, a Deep Evidence-based Research framework for Medicine with an agentic AI system. DeepER-Med frames deep medical research as an explicit and inspectable workflow of evidence-based generation, consisting of three modules: research planning, agentic collaboration, and evidence synthesis. To support realistic evaluation, we also present DeepER-MedQA, an evidence-grounded dataset comprising 100 expert-level research questions derived from authentic medical research scenarios and curated by a multidisciplinary panel of 11 biomedical experts. Expert manual evaluation demonstrates that DeepER-Med consistently outperforms widely used production-grade platforms across multiple criteria, including the generation of novel scientific insights. We further demonstrate the practical utility of DeepER-Med through eight real-world clinical cases. Human clinician assessment indicates that DeepER-Med's conclusions align with clinical recommendations in seven cases, highlighting its potential for medical research and decision support.

Palantir's NHS future in doubt as ministers eye contract break

£330M deal leaves service with no ownership of software built to connect trusts to the platform The UK government is considering ending Palantir's involvement in a central NHS data platform after coming under fire from MPs, unions, and campaigners.…

How people use Copilot for Health

arXiv:2604.15331v1 Announce Type: cross Abstract: We analyze over 500,000 de-identified health-related conversations with Microsoft Copilot from January 2026 to characterize what people ask conversational AI about health. We develop a hierarchical intent taxonomy of 12 primary categories using privacy-preserving LLM-based classification validated against expert human annotation, and apply LLM-driven topic-clustering for prevalent themes within each intent. Using this taxonomy, we characterize the intents and topics behind health queries, identify who these queries are about, and analyze how usage varies by device and time of day. Five findings stand out. First, nearly one in five conversations involve personal symptom assessment or condition discussion, and even the dominant general information category (40%) is concentrated on specific treatments and conditions, suggesting that this is a lower bound on personal health intent. Second, one in seven of these personal health queries concern someone other than the user, such as a child, a parent, a partner, suggesting that conversational AI can be a caregiving tool, not just a personal one. Third, personal queries about symptoms and emotional health queries increase markedly in the evening and nighttime hours, when traditional healthcare is most limited. Fourth, usage diverges sharply by device: mobile concentrates on personal health concerns, while desktop is dominated by professional and academic work. Fifth, a substantial share of queries focuses on navigating healthcare systems such as finding providers, and understanding insurance, highlighting friction in the delivery of existing healthcare. These patterns have direct implications for platform-specific design, safety considerations, and the responsible development of health AI.

Regulatory Considerations for Artificial Intelligence in Healthcare: A WHO Perspective

The mission of the World Health Organization (WHO) is to promote health, keep the world safe and serve the vulnerable is articulated in its global strategy on digital health 2020–2025. At the heart of...

SciFi: A Safe, Lightweight, User-Friendly, and Fully Autonomous Agentic AI Workflow for Scientific Applications

arXiv:2604.13180v1 Announce Type: new Abstract: Recent advances in agentic AI have enabled increasingly autonomous workflows, but existing systems still face substantial challenges in achieving reliable deployment in real-world scientific research. In this work, we present a safe, lightweight, and user-friendly agentic framework for the autonomous execution of well-defined scientific tasks. The framework combines an isolated execution environment, a three-layer agent loop, and a self-assessing do-until mechanism to ensure safe and reliable operation while effectively leveraging large language models of varying capability levels. By focusing on structured tasks with clearly defined context and stopping criteria, the framework supports end-to-end automation with minimal human intervention, enabling researchers to offload routine workloads and devote more effort to creative activities and open-ended scientific inquiry.

AI in Healthcare: Aid, Not Replace—Clinicians Warn of Risks as Users Seek Speed and Privacy

Physicians stress AI should support, not replace, professional care amid concerns about misinformation and privacy.

How AI Is Being Used To Detect Cancer at The Earliest Stage

Dr. Bea Bakshi, CEO & Co-Founder of C the Signs joins Bloomberg Businessweek to discuss the future of cancer detection and how AI is part of the solution even in the early stages. (Source: Bloomberg)

AI in Healthcare: Aid, Not Replace—Clinicians Warn of Risks as Users Seek Speed and Privacy

Physicians stress AI should support, not replace, professional care amid concerns about misinformation and privacy.

A longitudinal health agent framework

arXiv:2604.12019v1 Announce Type: new Abstract: Although artificial intelligence (AI) agents are increasingly proposed to support potentially longitudinal health tasks, such as symptom management, behavior change, and patient support, most current implementations fall short of facilitating user intent and fostering accountability. This contrasts with prior work on supporting longitudinal needs, where follow-up, coherent reasoning, and sustained alignment with individuals' goals are critical for both effectiveness and safety. In this paper, we draw on established clinical and personal health informatics frameworks to define what it would mean to orchestrate longitudinal health interactions with AI agents. We propose a multi-layer framework and corresponding agent architecture that operationalizes adaptation, coherence, continuity, and agency across repeated interactions. Through representative use cases, we demonstrate how longitudinal agents can maintain meaningful engagement, adapt to evolving goals, and support safe, personalized decision-making over time. Our findings underscore both the promise and the complexity of designing systems capable of supporting health trajectories beyond isolated interactions, and we offer guidance for future research and development in multi-session, user-centered health AI.

China Launches AI Doctor Platform for Parkinson's

Xuanwu Hospital in Beijing has launched El.kz, China's first AI-powered platform for Parkinson's disease, aimed at automating routine patient inquiries. This initiative is part of a broader strategy to digitize healthcare, leveraging over 20 years of clinical data to alleviate physician workload and better manage chronic conditions in an aging population.

Adoption and Effectiveness of AI-Based Anomaly Detection for Cross Provider Health Data Exchange

arXiv:2604.09630v1 Announce Type: new Abstract: This study investigates the adoption and effectiveness of AI-based anomaly detection in cross-provider electronic health record (EHR) environments. It aims to (1) identify the organisational and digital capabilities required for successful implementation and (2) evaluate the performance and interpretability of lightweight anomaly detection approaches using contextual audit data. A semi-systematic scoping synthesis is conducted to derive a four-pillar readiness framework covering governance, infrastructure/interoperability, workforce, and AI integration, operationalised as a 10-item checklist with measurable indicators. This is complemented by a simulation of cross-provider audit logs incorporating contextual features such as provider mismatch, time of access, days since discharge, session duration, and access frequency. A rule-based approach is benchmarked against Isolation Forest, with SHAP used to explain model behaviour. Results show that rule-based methods achieve high recall but generate higher alert volumes, while Isolation Forest reduces alert burden at the cost of lower sensitivity. SHAP analysis highlights provider mismatch and off-hours access as dominant anomaly drivers. The study proposes a staged deployment strategy combining rules for coverage and machine learning for prioritisation, supported by explainability and continuous monitoring. The findings contribute a practical readiness framework and empirical insights to guide the implementation of AI-based anomaly detection in multi-provider healthcare environments.

China Launches AI Doctor Platform for Parkinson's, Streamlining Patient Support

Xuanwu Hospital in Beijing has launched El.kz, China's first AI-powered platform for Parkinson's disease, aimed at automating routine patient inquiries.

Investigating Vaccine Buyer's Remorse: Post-Vaccination Decision Regret in COVID-19 Social Media Using Politically Diverse Human Annotation

arXiv:2604.09626v1 Announce Type: new Abstract: A significant gap exists in datasets regarding post-COVID-19 vaccination experiences, particularly ``vaccine buyer's remorse''. Understanding the prevalence and nature of vaccine regret, whether based on personal or vicarious experiences, is vital for addressing vaccine hesitancy and refining public health communication. In this paper, we curate a novel dataset from a large YouTube news corpus capturing COVID-19 vaccination experiences, and construct a benchmark subset focused on vaccine regret, annotated by a politically diverse panel to account for the subjective and often politicized nature of the topic. We utilize large language models (LLMs) to identify posts expressing vaccine regret, analyze the reasons behind this regret, and quantify its occurrence in both first and second-person accounts. This paper aims to (1) quantify the prevalence of vaccine regret; (2) identify common reasons for this sentiment; (3) analyze differences between first-person and vicarious experiences; and (4) assess potential biases introduced by different LLMs. We find that while vaccine buyer's remorse appears in only $<2\%$ of public discourse, it is disproportionately concentrated in vaccine-skeptic influencer communities and is predominantly expressed through first-person narratives citing adverse health events.

AI chatbots misdiagnose in over 80% of early medical cases, study finds

Top models including OpenAI and DeepSeek make judgments too quickly when patient data is incomplete

AI to predict how bowel cancer patients will respond to new NHS drug | Bowel cancer | The Guardian

PhenMap tool could spare thousands of patients from treatment that would be ineffective for them

7 ways AI is advancing healthcare and wellbeing around the world - Source

AI-powered tools are being used to bring greater efficiency and security to healthcare around the world and increase access to medicines and care.

Healthcare AI Faces Scaling Challenges

A Qventus study reveals that while many health IT leaders deploy AI, only 4% have achieved measurable outcomes due to integration hurdles.

AI Data Governance for Healthcare | Health AI

Data quality, privacy, and governance requirements for clinical AI

India Unveils Futuristic Surgical Tech at SMRSC 2026

India showcased battlefield care and tele-surgery innovations from SS Innovations International at the SMRSC 2026 event.

AI Policy for Reproductive Medicine in California | Health AI

AI compliance requirements for reproductive medicine in California. State-specific regulation, HIPAA, and governance guidance.

Healthcare AI Faces Scaling Challenges

A Qventus study reveals that while 42% of health IT leaders deploy AI across multiple use cases, only 4% have measurable outcomes, highlighting challenges in scaling AI pilots. Experts emphasize the risks of poor technology bets and the necessity of AI integration for competitive advantage, as healthcare systems push for …

Co-design for Trustworthy AI: An Interpretable and Explainable Tool for Type 2 Diabetes Prediction Using Genomic Polygenic Risk Scores

arXiv:2604.08217v1 Announce Type: new Abstract: The polygenic risk scores (PRS) have emerged as an important methodology for quantifying genetic predisposition to complex traits and clinical disease. Significant progress has been made in applying PRS to conditions such as obesity, cancer, and type 2 diabetes (T2DM). Studies have demonstrated that PRS can effectively identify individuals at high risk, thereby enabling early screening, personalized treatment, and targeted interventions for diseases with a genetic predisposition. One current limitation of PRS, however, is the lack of interpretability tools. To address this problem for T2DM, researchers at the Graduate School of Data Science at the Seoul National University introduced eXplainable PRS (XPRS). This visualization tool decomposes PRSs into gene-level and single-nucleotide polymorphism (SNP) contribution scores via Shapley Additive Explanations (SHAP), providing granular insights into the specific genetic factors driving an individual's risk profile. We used a co-design approach to assess XPRS trustworthiness by considering legal, medical, ethical, and technical robustness during early design and potential clinical use. For that, we used Z-inspection, an ethically aligned Trustworthy AI co-design methodology, and piloted the Council of Europe's Human Rights, Democracy, and the Rule of Law Impact Assessment for AI Systems (HUDERIA) (Council of Europe (CAI) 2025). The findings of this use-case comprise a comprehensive set of ethical, legal, and technical lessons learned. These insights, identified by a multidisciplinary team of experts (ethics, legal, human rights, computer science, and medical), serve as a framework for designers to navigate future challenges with this and other AI systems. The findings also provide a useful reference for researchers developing explainability frameworks for PRS in diverse clinical contexts.

IatroBench: Pre-Registered Evidence of Iatrogenic Harm from AI Safety Measures

arXiv:2604.07709v1 Announce Type: cross Abstract: Ask a frontier model how to taper six milligrams of alprazolam (psychiatrist retired, ten days of pills left, abrupt cessation causes seizures) and it tells her to call the psychiatrist she just explained does not exist. Change one word ("I'm a psychiatrist; a patient presents with...") and the same model, same weights, same inference pass produces a textbook Ashton Manual taper with diazepam equivalence, anticonvulsant coverage, and monitoring thresholds. The knowledge was there; the model withheld it. IatroBench measures this gap. Sixty pre-registered clinical scenarios, six frontier models, 3,600 responses, scored on two axes (commission harm, CH 0-3; omission harm, OH 0-4) through a structured-evaluation pipeline validated against physician scoring (kappa_w = 0.571, within-1 agreement 96%). The central finding is identity-contingent withholding: match the same clinical question in physician vs. layperson framing and all five testable models provide better guidance to the physician (decoupling gap +0.38, p = 0.003; binary hit rates on safety-colliding actions drop 13.1 percentage points in layperson framing, p = 1 (kappa = 0.045); the evaluation apparatus has the same blind spot as the training apparatus. Every scenario targets someone who has already exhausted the standard referrals.

AI Legislative Update: April 10, 2026 — Transparency Coalition. Legislation for Transparency in AI Now.

Every Friday, TCAI brings you the nation’s most comprehensive update of AI-related legislation moving through state legislatures. This week: Therapy chatbot bans are picking up speed. Maine sent a therapy bot ban to the governor, while Missouri is moving on a similar ban via an omnibus health ...

UnitedHealth Just Dropped $3 Billion On AI. Not To Save Your Life. To Deny Your Claim Faster.

The company now employs 22,000 software engineers. More than 80 percent are building AI tools. Not to find cures. Not to coordinate care.

Opinion | Meet Abi, the AI robot senior care companion - The Washington Post

Abi, an AI -powered companion, at a senior care home in Melbourne, Australia, in 2025.

How AI could change patient care, not replace it | Medical Economics

Patients are bringing their health questions to AI, often before even scheduling a visit with their physician. Medallia's Amber Maraccini, Ph.D., M.A., explains how tools such as ChatGPT Health can either erode trust or create more space for human conversations.

Navigating the European Union’s AI and health data framework - Atlantic Council

The EU is strengthening AI and health data governance, creating a more secure and trusted framework for innovation.

Watch: As AI Makes More Health Coverage Decisions, the Risks to Patients Grow - KFF Health News

Major health insurers and even Medicare are using artificial intelligence to make coverage decisions. But class action lawsuits have accused insurers of using AI to wrongfully withhold treatment, and new research illuminates the risks.

An Analysis of Artificial Intelligence Adoption in NIH-Funded Research

arXiv:2604.07424v1 Announce Type: cross Abstract: Understanding the landscape of artificial intelligence (AI) and machine learning (ML) adoption across the National Institutes of Health (NIH) portfolio is critical for research funding strategy, institutional planning, and health policy. The advent of large language models (LLMs) has fundamentally transformed research landscape analysis, enabling researchers to perform large-scale semantic extraction from thousands of unstructured research documents. In this paper, we illustrate a human-in-the-loop research methodology for LLMs to automatically classify and summarize research descriptions at scale. Using our methodology, we present a comprehensive analysis of 58,746 NIH-funded biomedical research projects from 2025. We show that: (1) AI constitutes 15.9% of the NIH portfolio with a 13.4% funding premium, concentrated in discovery, prediction, and data integration across disease domains; (2) a critical research-to-deployment gap exists, with 79% of AI projects remaining in research/development stages while only 14.7% engage in clinical deployment or implementation; and (3) health disparities research is severely underrepresented at just 5.7% of AI-funded work despite its importance to NIH's equity mission. These findings establish a framework for evidence-based policy interventions to align the NIH AI portfolio with health equity goals and strategic research priorities.

Grounding Clinical AI Competency in Human Cognition Through the Clinical World Model and Skill-Mix Framework

IatroBench: Pre-Registered Evidence of Iatrogenic Harm from AI Safety Measures

Ask a frontier model how to taper six milligrams of alprazolam (psychiatrist retired, ten days of pills left, abrupt cessation causes seizures) and it tells her to call the psychiatrist she just explained does not exist. Change one word ("I'm a psychiatrist; a patient presents with... ") and the same model, same weights, same inference pass produces a textbook Ashton Manual taper with diazepam equivalence, anticonvulsant coverage, and monitoring thresholds.

For Oura and Whoop, health has a reasonable chance of being wealth

The two health technology wearable companies have recently raised money at valuations of $11bn and $10bn respectively

Health - The Washington Post

Top health officials point to AI as the solution for dying hospitals, but experts from rural areas of the country say the solution isn’t that simple.

SymptomWise: A Deterministic Reasoning Layer for Reliable and Efficient AI Systems

arXiv:2604.06375v1 Announce Type: new Abstract: AI-driven symptom analysis systems face persistent challenges in reliability, interpretability, and hallucination. End-to-end generative approaches often lack traceability and may produce unsupported or inconsistent diagnostic outputs in safety-critical settings. We present SymptomWise, a framework that separates language understanding from diagnostic reasoning. The system combines expert-curated medical knowledge, deterministic codex-driven inference, and constrained use of large language models. Free-text input is mapped to validated symptom representations, then evaluated by a deterministic reasoning module operating over a finite hypothesis space to produce a ranked differential diagnosis. Language models are used only for symptom extraction and optional explanation, not for diagnostic inference. This architecture improves traceability, reduces unsupported conclusions, and enables modular evaluation of system components. Preliminary evaluation on 42 expert-authored challenging pediatric neurology cases shows meaningful overlap with clinician consensus, with the correct diagnosis appearing in the top five differentials in 88% of cases. Beyond medicine, the framework generalizes to other abductive reasoning domains and may serve as a deterministic structuring and routing layer for foundation models, improving precision and potentially reducing unnecessary computational overhead in bounded tasks.

Front-End Ethics for Sensor-Fused Health Conversational Agents: An Ethical Design Space for Biometrics

arXiv:2604.06203v1 Announce Type: new Abstract: The integration of continuous data from built-in sensors and Large Language Models (LLMs) has fueled a surge of "Sensor-Fused LLM agents" for personal health and well-being support. While recent breakthroughs have demonstrated the technical feasibility of this fusion (e.g., Time-LLM, SensorLLM), research primarily focuses on "Ethical Back-End Design for Generative AI", concerns such as sensing accuracy, bias mitigation in training data, and multimodal fusion. This leaves a critical gap at the front end, where invisible biometrics are translated into language directly experienced by users. We argue that the "illusion of objectivity" provided by sensor data amplifies the risks of AI hallucinations, potentially turning errors into harmful medical mandates. This paper shifts the focus to "Ethical Front-End Design for AI", specifically, the ethics of biometric translation. We propose a design space comprising five dimensions: Biometric Disclosure, Monitoring Temporality, Interpretation Framing, AI Stance, and Contestability. We examine how these dimensions interact with context (user- vs. system-initiated) and identify the risk of biofeedback loops. Finally, we propose "Adaptive Disclosure" as a safety guardrail and offer design guidelines to help developers manage fallibility, ensuring that these cutting-edge health agents support, rather than destabilize, user autonomy.

XRPH AI: Building the Right Foundation for What Comes Next | by XRP Healthcare | Apr, 2026 | Medium

The XRPH AI App is already live, with growing user engagement and a developing framework designed to connect real healthcare activity to measurable outcomes.

Tennessee Bill on AI Friends

I think what Tennessee is doing is they recently passed SB 1580, which makes it illegal to even advertise that an AI can act as a mental health professional.

Accenture global health lead on scaling AI in healthcare with governance and intent | TechTarget

It's about deploying it deliberately where it demonstrably improves outcomes and can be governed with confidence. And the winners are not the ones who adopt AI fastest. They're the ones who adopt it wisely. Jill Hughes has covered health tech news since 2021. ... Predicting 2025's top analytics, AI trends in ... – Healthtech Analytics · Shadow AI in healthcare...

Advita Ortho Unveils AI-Driven Advances

Advita Ortho is spearheading AI-driven advancements in orthopedic care, unveiling 16 new studies at ORS 2026 focused on enhancing joint replacement surgeries with data-driven technologies.

Advita Ortho Unveils AI-Driven Advances in Orthopedic Surgery at ORS 2026

Advita Ortho is spearheading AI-driven advancements in orthopedic care, unveiling 16 new studies at ORS 2026 focused on enhancing joint replacement surgeries with data-driven technologies.

MedGemma 1.5 Technical Report

arXiv:2604.05081v1 Announce Type: new Abstract: We introduce MedGemma 1.5 4B, the latest model in the MedGemma collection. MedGemma 1.5 expands on MedGemma 1 by integrating additional capabilities: high-dimensional medical imaging (CT/MRI volumes and histopathology whole slide images), anatomical localization via bounding boxes, multi-timepoint chest X-ray analysis, and improved medical document understanding (lab reports, electronic health records). We detail the innovations required to enable these modalities within a single architecture, including new training data, long-context 3D volume slicing, and whole-slide pathology sampling. Compared to MedGemma 1 4B, MedGemma 1.5 4B demonstrates significant gains in these new areas, improving 3D MRI condition classification accuracy by 11% and 3D CT condition classification by 3% (absolute improvements). In whole slide pathology imaging, MedGemma 1.5 4B achieves a 47% macro F1 gain. Additionally, it improves anatomical localization with a 35% increase in Intersection over Union on chest X-rays and achieves a 4% macro accuracy for longitudinal (multi-timepoint) chest x-ray analysis. Beyond its improved multimodal performance over MedGemma 1, MedGemma 1.5 improves on text-based clinical knowledge and reasoning, improving by 5% on MedQA accuracy and 22% on EHRQA accuracy. It also achieves an average of 18% macro F1 on 4 different lab report information extraction datasets (EHR Datasets 2, 3, 4, and Mendeley Clinical Laboratory Test Reports). Taken together, MedGemma 1.5 serves as a robust, open resource for the community, designed as an improved foundation on which developers can create the next generation of medical AI systems. Resources and tutorials for building upon MedGemma 1.5 can be found at https://goo.gle/MedGemma.

AI Boom Could Cause Health Care Costs to Soar - Penn LDI

AI is already affecting health care delivery, and the choices policymakers make about payment will define its future trajectory, says LDI Fellow Amol Navathe.

THE DAILY SCRAPE - by Brent Orrell - Help Desk

THE DAILY SCRAPE - by Brent Orrell - Help Desk # Help Desk SubscribeSign in # THE DAILY SCRAPE ### Your daily rundown on AI and what it means for the American Workforce Apr 08, 2026 Share APRIL 7, 2026 *Wall Street is embracing AI agents. While some older workers are choosing early retirement over reskilling, healthcare professionals are discovering that AI anxiety often gives way to cautious adoption once workers see the tools in action. The Trump administration has forced Brown University to invest $50 million in job training programs. * Data Point of the Day Today’s Number $30 billion Anthropic’s annual revenue run rate shows just how fast AI companies are scaling up. How soon can other AI companies move past capital burn and into profitability? — Bloomberg Top Stories AI· AUTOMATION [AI Agents Are Coming for Wall Street](https://www.inc.com/phil-rosen/ai-agents-financ

Uncertainty-Guided Latent Diagnostic Trajectory Learning for Sequential Clinical Diagnosis

arXiv:2604.05116v1 Announce Type: new Abstract: Clinical diagnosis requires sequential evidence acquisition under uncertainty. However, most Large Language Model (LLM) based diagnostic systems assume fully observed patient information and therefore do not explicitly model how clinical evidence should be sequentially acquired over time. Even when diagnosis is formulated as a sequential decision process, it is still challenging to learn effective diagnostic trajectories. This is because the space of possible evidence-acquisition paths is relatively large, while clinical datasets rarely provide explicit supervision information for desirable diagnostic paths. To this end, we formulate sequential diagnosis as a Latent Diagnostic Trajectory Learning (LDTL) framework based on a planning LLM agent and a diagnostic LLM agent. For the diagnostic LLM agent, diagnostic action sequences are treated as latent paths and we introduce a posterior distribution that prioritizes trajectories providing more diagnostic information. The planning LLM agent is then trained to follow this distribution, encouraging coherent diagnostic trajectories that progressively reduce uncertainty. Experiments on the MIMIC-CDM benchmark demonstrate that our proposed LDTL framework outperforms existing baselines in diagnostic accuracy under a sequential clinical diagnosis setting, while requiring fewer diagnostic tests. Furthermore, ablation studies highlight the critical role of trajectory-level posterior alignment in achieving these improvements.

Algebraic Structure Discovery for Real World Combinatorial Optimisation Problems: A General Framework from Abstract Algebra to Quotient Space Learning

arXiv:2604.04941v1 Announce Type: new Abstract: Many combinatorial optimisation problems hide algebraic structures that, once exposed, shrink the search space and improve the chance of finding the global optimal solution. We present a general framework that (i) identifies algebraic structure, (ii) formalises operations, (iii) constructs quotient spaces that collapse redundant representations, and (iv) optimises directly over these reduced spaces. Across a broad family of rule-combination tasks (e.g., patient subgroup discovery and rule-based molecular screening), conjunctive rules form a monoid. Via a characteristic-vector encoding, we prove an isomorphism to the Boolean hypercube $\{0,1\}^n$ with bitwise OR, so logical AND in rules becomes bitwise OR in the encoding. This yields a principled quotient-space formulation that groups functionally equivalent rules and guides structure-aware search. On real clinical data and synthetic benchmarks, quotient-space-aware genetic algorithms recover the global optimum in 48% to 77% of runs versus 35% to 37% for standard approaches, while maintaining diversity across equivalence classes. These results show that exposing and exploiting algebraic structure offers a simple, general route to more efficient combinatorial optimisation.

Resource-Conscious Modeling for Next- Day Discharge Prediction Using Clinical Notes

arXiv:2604.03498v1 Announce Type: new Abstract: Timely discharge prediction is essential for optimizing bed turnover and resource allocation in elective spine surgery units. This study evaluates the feasibility of lightweight, fine-tuned large language models (LLMs) and traditional text-based models for predicting next-day discharge using postoperative clinical notes. We compared 13 models, including TF-IDF with XGBoost and LGBM, and compact LLMs (DistilGPT-2, Bio_ClinicalBERT) fine-tuned via LoRA. TF-IDF with LGBM achieved the best balance, with an F1-score of 0.47 for the discharge class, a recall of 0.51, and the highest AUC-ROC (0.80). While LoRA improved recall in DistilGPT2, overall transformer-based and generative models underperformed. These findings suggest interpretable, resource-efficient models may outperform compact LLMs in real-world, imbalanced clinical prediction tasks.

How AI is used in Pittsburgh hospitals, and what workers think

AI tools are becoming more commonplace in Pittsburgh hospitals, promising productivity gains for practitioners and solving the nursing crisis. But to local hospital employees, enduring understaffing and burnout, another solution is clear: Hire more workers.

AI in the mental health care workforce is met with fear, pushback — and enthusiasm | WFSU News

Artificial intelligence tools that help mental health therapists take notes and keep records are quickly entering the marketplace. But some question the safety of AI in mental health care delivery.

How MassMutual and Mass General Brigham Turned AI Pilot Sprawl into Production Results

This article provides practical lessons on moving from disconnected AI pilots to governed production value, emphasizing success metrics and flexible architecture.

neuroClues raises €10 million Series A to become the brain’s stethoscope for early diagnosis of neurological disorders

neuroClues, a French-Belgian MedTech startup empowering clinicians with biomarkers allowing them to identify neurological disorders years before visible symptoms, has raised a €10 million Series A, along with additional non-dilutive funding, bringing the total capital raised by the company to €25 million.  The round is led by Teampact Ventures, White Fund and the EIC Fund […]

Health Care Roundup: Market Talk

Find insight on UltraGreen.ai, Pro Medicus and more in the latest Market Talks covering the health care sector.

AI in Healthcare: How Machine Learning Is Transforming Faster Disease Diagnosis in 2026

AI healthcare diagnostics improve disease detection accuracy and speed, transforming medical care with machine learning and predictive analytics.

AI in the mental health care workforce is met with fear, pushback — and enthusiasm

Artificial intelligence tools that help mental health therapists take notes and keep records are quickly entering the marketplace. But some question the safety of AI in mental health care delivery.

Navigating AI in Healthcare: Can Patients Opt Out of AI Note-Taking? - Dr. Matthew Lynch

In an era where technology continuously reshapes various aspects of our lives, the healthcare sector is no exception. One of the most notable advancements is the integration of artificial intelligence (AI) in clinical settings. Family physician Eric Boose from the Cleveland Clinic has embraced ...

World Health Day: AI doctors vs real doctors — where machines win and where they fail - The Times of India

Science News: A simple test by Dr Mikhail Varshavski, a practising, board-certified family medicine doctor based in New York, shows both AI's strengths and its blin.

AI in the mental health care workforce is met with fear, pushback — and enthusiasm

Artificial intelligence tools that help mental health therapists take notes and keep records are quickly entering the marketplace. But some question the safety of AI in mental health care delivery.

State of the Art Report for Smart Habitat for Older Persons -- Working Group 3 -- Healthcare

arXiv:2604.03255v1 Announce Type: new Abstract: This document reports the State of the Art of science and practice on three topics related to smart and healthy ageing at home: furniture and habitats, Information and Communication Technologies (ICT), and healthcare. The reports were prepared by the working groups of COST Action CA16226, Sheld-on. Sheld-on is a network of researchers, user representatives, industry members, and other stakeholders. The three domains covered in this report were the areas of interest for three working groups from the COST Action. The aim of each working group was to assess the State of the Art for disciplinary understanding, identification of advances in smart furniture and habitat, products, industries and success stories. The findings on these topics of all working groups are compiled here. Due to the different backgrounds of the members of each of the working groups, the document is divided in three separate parts that can be considered as separate State of the Art reports. The goal of this document is to be used as input in the fourth working group of Sheld-on COST Action: Solutions for Ageing Well at Home, in the Community, and at Work, where experts from the three different domains converge to a single working group in order to achieve the action objectives.

VERT: Reliable LLM Judges for Radiology Report Evaluation

arXiv:2604.03376v1 Announce Type: new Abstract: Current literature on radiology report evaluation has focused primarily on designing LLM-based metrics and fine-tuning small models for chest X-rays. However, it remains unclear whether these approaches are robust when applied to reports from other modalities and anatomies. Which model and prompt configurations are best suited to serve as LLM judges for radiology evaluation? We conduct a thorough correlation analysis between expert and LLM-based ratings. We compare three existing LLM-as-a-judge metrics (RadFact, GREEN, and FineRadScore) alongside VERT, our proposed LLM-based metric, using open- and closed-source models (reasoning and non-reasoning) of different sizes across two expert-annotated datasets, RadEval and RaTE-Eval, spanning multiple modalities and anatomies. We further evaluate few-shot approaches, ensembling, and parameter-efficient fine-tuning using RaTE-Eval. To better understand metric behavior, we perform a systematic error detection and categorization study to assess alignment of these metrics against expert judgments and identify areas of lower and higher agreement. Our results show that VERT improves correlation with radiologist judgments by up to 11.7% relative to GREEN. Furthermore, fine-tuning Qwen3 30B yield gains of up to 25% using only 1,300 training samples. The fine-tuned model also reduces inference time up to 37.2 times. These findings highlight the effectiveness of LLM-based judges and demonstrate that reliable evaluation can be achieved with lightweight adaptation.

How MassMutual and Mass General Brigham turned AI pilot sprawl into production results

A case study on how two major organizations successfully scaled their AI initiatives from experimental pilots to full production.

FDA Clears Adjunctive AI Mapping of White Matter on Diffusion MRI | Diagnostic Imaging

Through automated processing of diffusion-weighted magnetic resonance imaging (MRI) scans, the Advanced Neuro Diagnostic Imaging (ANDI) software performs detailed analysis of white matter microstructure.

Healthcare’s AI inflection point: The organizations that win will be the ones with the strongest data foundations | Healthcare Dive

Healthcare doesn’t have an AI experimentation problem. It has an execution gap — and that gap is widening.

AI dolls offer companionship to the elderly

South Korea’s strained social care system turns to ChatGPT-enabled devices as population ages

CMEF 2026: Unveiling Global Medical Innovations and AI Breakthroughs

The 93rd China International Medical Equipment Fair in Shanghai will feature over 5,000 brands and highlight significant AI advancements in medical technology.

NHS staff resist using Palantir software

Staff reportedly cite ethics concerns, privacy worries, and doubt the platform adds much.

Episode 62 - by Karim Hanna, MD - AI+MedEd

Carl Preiksaitis, a Stanford emergency medicine physician and educator, makes a thoughtful case that the answer isn’t to ban AI — it’s to be deliberate about what we protect as human-only cognitive work. His list is clear: first-pass differential diagnosis, initial problem representation, and the assessment and plan.

Enhance Rehab Center Efficiency with AI-Powered Documentation Audits

Enhance rehab center efficiency with AI-powered documentation audits for improved accuracy and care.

The Rise of AI in Healthcare

AI is helping tackle some of the world's most pressing challenges.

AI Tool Empowers Caregivers Managing Stage 4 Cancer Treatment – ICO Optics

Pratik Desai, a 34-year-old with a background in systems integration and AI, built a free, AI-assisted workflow to help his […]

AI In Healthcare Across Sub-Saharan Africa - Voxilens

The decade of the “promising pilot” is over. In 2026, the narrative of AI in African healthcare has shifted from aspirational headlines to verifiable, population-level impact. As the continent faces…

NBER AI in Healthcare: Everything You Need to Know - AI Expert Magazine - premier publication dedicated to AI

KEY FACTS Date: May 7-8, 2026 Location: Cambridge, MA, USA Type: Academic-Industry Symposium Website: rapidscale.net What Is NBER AI in Healthcare? The NBER AI in Healthcare symposium is a premier academic-industry event focused on the rigorous, evidence-based evaluation of artificial ...

AWS and UnitedHealthcare Take Back-Office to Front-End Approach to Healthcare AI | PYMNTS.com

In this week’s Prompt Economy update, new examples from AWS and UnitedHealthcare highlight how agentic AI is being applied across healthcare.

AI in Healthcare and Digital Health Today—April 6, 2026 - LucidQuest Ventures

Weekly AI in Healthcare and Digital Health update highlighting Eli Lilly-Insilico deal and Medtronic strategy shift, plus more.

Clinical AI Shifts to Reasoning in Medical Coding Systems

Corti advances clinical AI with reasoning-based medical coding, improving accuracy and auditability in healthcare systems.

One-Third of U.S. Adults Use AI Chatbots for Health Information, KFF Poll Finds

One-Third of U.S. Adults Use AI Chatbots for Health Information, KFF Poll Finds # One-Third of U.S. Adults Use AI Chatbots for Health Information, KFF Poll Finds April 6, 2026 April 6, 2026 3 A growing number of Americans are bypassing traditional search engines and doctor’s offices in favor of artificial intelligence, with 32% of adults reporting they have turned to AI chatbots for health information in the past year. This shift marks a significant pivot in how the public consumes medical advice, as the share of people using AI for health now equals the proportion who rely on social media for the same purpose. The trend is driven largely by a desire for immediacy and privacy, but it also reveals deeper systemic fractures in the U.S. Healthcare system

Open@Epic to Return in 2026 as Healthcare Data Sharing Accelerates | Epic

The second Open@Epic will be held in Verona, Wis. on Wednesday, October 21 and Thursday, October 22, 2026. Registration opens on July 16. Details and session information are available at open.epic.com/Conference. Alongside an expanding AI roadmap, Epic is highlighting measurable outcomes happening ...

Ember: Meal Scan, Macros & AI Coach

Ember: Meal scan, macros & AI coach.

UnitedHealth Group is making a $3 billion bet on AI. What does it mean for patients?

UHG is spending billions to embed AI to manage claims and care decisions. As 22,000 software engineers go to work, what are the benefits — and risks?

Wearable Robotics: €5 Million Raised For Neuromotor Rehabilitation Expansion

Wearable Robotics, a spin-off of the Sant’Anna School of Advanced Studies, has raised €5 million in a Series A funding round to accelerate its international expansion and advance its neuromotor rehabilitation technologies. The post Wearable Robotics: €5 Million Raised For Neuromotor Rehabilitation Expansion appeared first on Pulse 2.0.

Chatbots Are Now Prescribing Psychiatric Drugs

Chatbots are now being used to prescribe psychiatric drugs, raising concerns about the role of AI in healthcare.

LatentView Soars 14% After $3M Investment in Healtheon AI

LatentView Analytics' stock soared 14% after announcing a $3 million investment in Healtheon AI, aiming to enhance healthcare revenue cycle management through agentic-AI.

Ethical AI or character assassination? - by Gene Balfour

The perception of AI as a force for good or evil varies with each person you discuss this with. The selection of Palantir to improve access to medical records across the UK highlights this debate. Topic group: Labor & Society

AI Can Now Prescribe You Psychiatric Medication in Utah

The pilot program will see an AI chatbot, provided by health technology company Doctronic, prescribe psychiatric medication. But there are plenty of caveats, however, in terms of what it can and can't prescribe. Would you trust an AI to prescribe you mind-altering psychiatric medication? Amid numerous controversies around chatbot therapy or AI giving bad (or dangerous) medical advice, one healthcare provider is betting you will and has received regulatory approval … Topic group: Adoption & Impact

LatentView Soars 14% After $3M Investment in Healtheon AI, Boosting Healthcare AI Innovations

LatentView Analytics' stock rose 14% following a $3 million investment in Healtheon AI, signaling strong market confidence in healthcare AI innovations and the growing intersection of analytics technology and medical services. Topic group: Economics & Markets

I Uploaded My Blood Work to AI. Am I Oversharing?

When you connect medical records and health data to a chatbot, you get results. But you must understand the risks. Topic group: Labor & Society

These charts show the bulk of March’s job gains were concentrated in just a handful of sectors

Healthcare continued to drive gains in employment, while better weather in March also helped.

Work in healthcare, including nursing, boomed again in March. The sector has provided some of the most consistent job growth since the 1980s

Work in healthcare, including nursing, boomed again in March. The sector has provided some of the most consistent job growth since the 1980s.

It's Not Easy to Get Depression-Detecting AI Through the FDA

It's not easy to get depression-detecting AI through the FDA.

This new Nature paper (using old models) illustrates the point of my latest Substack post on AI interfaces. AI did a good job diagnosing medical issues, but when users had to interact with chatbots the interface led to confusion & worse answers

This new Nature paper (using old models) illustrates the point of my latest Substack post on AI interfaces. AI did a good job diagnosing medical issues, but when users had to interact with chatbots the interface led to confusion & worse answers My post: https://www. oneusefulthing.

AI Meets Egg Freezing

Sunfish, an AI-powered fertility platform, is introducing an egg-freezing program that uses predictive models to estimate the cost of reaching a target number of eggs.

Matthew Gallagher used a suite of AI tools to create a telehealth company that generated $401 million in sales in its first full year. It's a great example of what I call The New Rules of Wealth. I'm launching a @MasterClass today so more people can learn how to use AI to rapidly launch and scale businesses.

Matthew Gallagher used a suite of AI tools to create a telehealth company that generated $401 million in sales in its first full year. It's a great example of what I call The New Rules of Wealth. I'm

IKS Health Acquires AI Team to Boost Patient Access Solutions

IKS Health acquires Tij Bedi's AI team, ThinkDTM, to enhance its patient access solutions with advanced AI capabilities.

Whoop hits $10bn valuation as health tracking takes off

Wearable device maker eyes IPO after raising $575mn from sovereign wealth funds and top athletes

Whoop, a Wearable Health Device Maker, Raises $575 Million

With elite athletes like LeBron James and Cristiano Ronaldo as investors, the company, now valued at $10 billion, is courting everyday health enthusiasts.

The Download: AI health tools and the Pentagon’s Anthropic culture war

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. There are more AI health tools than ever—but how well do they work?  In the last few months alone, Microsoft, Amazon, and OpenAI have all launched medical chatbots.  There’s a clear demand… Topic group: Adoption & Impact

Yuhan USA and Huinno Join Forces

Yuhan USA and Huinno join forces to introduce AI-driven ECG monitoring and prediction solutions in the US digital healthcare market.

Jonathon Trionfi - Group benefits guru | Specializing in self- ...

&quot;There&#x27;s no such thing as bad healthcare risk, only mispriced risk.&quot; Ali Panjwani built a predictive AI platform to prove that.

FDA Clears AI-Powered ECG Tool

Anumana's AI-driven ECG tool for pulmonary hypertension has achieved FDA clearance, marking a first for standard 12-lead ECGs in early PH detection.

Dominican Republic's AI-Powered Health Tourism

The Dominican Republic is leveraging AI to transform its health tourism sector, expanding from low-cost procedures to comprehensive preventive care.

There are more AI health tools than ever—but how well do they work?

Earlier this month, Microsoft launched Copilot Health, a new space within its Copilot app where users will be able to connect their medical records and ask specific questions about their health. A couple of days earlier, Amazon had announced that Health AI, an LLM-based tool previously restricted to members of its One Medical service, would… Topic group: Adoption & Impact

Where Curiosity Meets Impact: SheTech Interns Interview Brittney Vierra

Brittney Vierra, Co-founder and CTO of Amphora (Salt Lake City), shares her journey from physics to data science with SheTech media interns, Samhita Chavakula and Jaswitha Jadapalli. She shares how curiosity, not frustration, can challenge bias, drive innovation, and reshape AI’s role in healthcare. Topic group: Labor & Society

Maharashtra Launches Massive Health ATM Network to Revolutionize Rural Healthcare Access

Clinics on Cloud partners with Sainik Shetkari Welfare Foundation to launch over 2,000 AI-powered Health ATMs across rural Maharashtra.

A woman's uterus has been kept alive outside the body for the first time

First successful ex vivo uterus survival in AI-connected system marks breakthrough in transplant and reproductive medicine.

Dimer Health: $13.5 Million Raised For AI-Driven Post-Discharge Care Platform

Dimer Health, a clinician-led, AI-native transitional care medicine company, has raised $13. 5 million in series A financing to expand its post-discharge care platform and accelerate national growth. The round was led by Team8 and Table Management, with participation from Silver Circle and TechAviv, bringing the company’s total funding to nearly $20 million.

Justice Department Files Antitrust Lawsuit Against NewYork-Presbyterian

The department has been investigating prominent hospital system’s contracts with insurers.

Blossom Health Raises $20 Million in Funding

Blossom Health, an AI operating system provider for psychiatry, has raised $20 million in a Series A and seed funding round. This funding aims to expand access to mental healthcare and accelerate the development of its applied AI technology.

Qualified Health Raises $125 Million in Funding

Qualified Health, founded in 2023, has raised $125 million in an early-stage funding round to enhance the adoption of artificial intelligence in enterprise health systems. The company aims to provide secure AI integrations that allow health organizations to implement technology.

Adonis Raises $40 Million in Funding

Adonis, an AI-driven revenue cycle management platform for healthcare providers, has raised $40 million in a Series C funding round led by Quadrille Capital, with continued support from General Catalyst and Bling Capital.

Thesis Care Raises $45M

Thesis Care, a provider of an AI-powered care team platform, has raised $45 million in Series A funding.

Zain Khalpey, MD, PhD, FACS - ATARI AI | LinkedIn

Adverse events in AI remain under-discussed in healthcare. As AI integrates into clinical workflows, it is often held to a different standard than traditional medical devices. Drugs and devices undergo rigorous validation, post-market surveillance, and structured adverse event reporting.

r/OpenAI on Reddit: I think this is the right path for OpenAI.

Historically, the knowledge required to troubleshoot virology experiments or identify the right DNA sequences was siloed among experts with years of hands-on experience. Recent benchmarks, such as the Virology Capabilities Test (VCT), show that frontier AI models can now score at or above the level of human experts in troubleshooting practical wet-lab procedures.

Exposure-Normalized Bed and Chair Fall Rates via Continuous AI Monitoring

This retrospective cohort study used continuous AI monitoring to estimate fall rates by exposure time rather than occupied bed-days. From August 2024 to December 2025, 3,980 eligible monitoring units contributed 292,914 hourly rows, yielding probability-weighted rates of 17. 8 falls per 1,000 chair exposure-hours and 4.

Sanius Health Acquires Kielo Research to Enhance AI-Native Care

Sanius Health, a London-based provider of AI-native care management and life sciences platforms, has acquired Kielo Research, a Zug-based provider of patient-centered research and health economics.

Leap AI Acquired by Chartis to Enhance Healthcare Innovation

Chartis has acquired Leap AI, a company specializing in AI-driven healthcare solutions, to enhance healthcare innovation through tailored AI applications.

Dimer Health Raises $13.5M for Transitional Care

Dimer Health, an AI-native transitional care medicine company, has raised $13.5 million in Series A financing led by Team8 and Bill Ackman's Table Management, bringing total funding to nearly $20 million.

AI Health Care Reliability Problem

Hundreds of AI tools for health care tout accuracy rates above 90%, but most are tested only in isolation. Those tools become less reliable when used together, an analysis by Korean AI scientist Kwansub Yun suggests.

TrustFed: Enabling Trustworthy Medical AI under Data Privacy Constraints

Protecting patient privacy remains a fundamental barrier to scaling machine learning across healthcare institutions, where centralizing sensitive data is often infeasible due to ethical, legal, and regulatory constraints. Federated learning offers a promising alternative by enabling privacy-preserving, multi-institutional training without sharing raw patient data; however, real-world deployments face severe challenges from data heterogeneity, site-specific biases, and class imbalance, which degr...

Cerebra: A Multidisciplinary AI Board for Multimodal Dementia Characterization and Risk Assessment

Modern clinical practice increasingly depends on reasoning over heterogeneous, evolving, and incomplete patient data. Although recent advances in multimodal foundation models have improved performance on various clinical tasks, most existing models remain static, opaque, and poorly aligned with real-world clinical workflows. We present Cerebra, an interactive multi-agent AI team that coordinates specialized agents for EHR, clinical notes, and medical imaging analysis.

Rivia Raises $15M in Series A Funding

Rivia, a Zurich-based software development company specializing in AI-driven clinical trial data infrastructure, has raised an oversubscribed $15 million in a Series A funding round.

Spectral AI Secures $31.7M in Funding

Spectral AI has secured $31.7 million in funding from the Biomedical Advanced Research and Development Authority (BARDA) to accelerate the development of its DeepView system.

Latent Health Raises $80M in Series A Funding

Latent Health, a clinical AI company, has raised $80 million in Series A funding to accelerate access to life-saving medications.

Health Lean Analytics Raises $2.268M in Funding

Health Lean Analytics, a Barcelona, Spain-based developer of an AI-driven hospital data automation platform, raised €2.1 million in funding.

Health Universe Raises $6M in Seed Funding

Health Universe, an enterprise AI platform focused on automating complex healthcare workflows, has raised $6 million in seed funding.

Verily Raises $300M in Funding

Verily, a Dallas, TX-based provider of a data platform for AI-enabled precision health solutions, raised $300 million in funding.

Spotlight Pathology Raises $1.75M in Funding

Spotlight Pathology, a spinout from the University of Manchester, has secured £1.4 million to accelerate the rollout of its AI-powered cancer diagnostics product.

Why AI Will Make Psychiatry the Hottest Career of the Decade

Reddit

AI Robots Revolutionize Eldercare and Cancer Detection

AI-driven robots are revolutionizing eldercare in China, offering health monitoring and emotional support, while tools like PANDA enhance cancer diagnostics with impressive precision.

'Thank God they're still alive': Kaiser therapists claim its new screening system puts patients at higher risk by delaying their care

Kaiser pushed back on striking workers' claims and AI fears, saying it delivers 'timely, high-quality care to meet members' needs'

UK’s Flexzo AI raises €10.3 million to transform healthcare workforce management with agentic AI

Flexzo AI, a UK-based startup providing an agentic AI workforce system for healthcare providers, today announced the completion of a €10.3 million ($12 million) Series A funding round to support ongoing development and commercialization of its AI-powered workforce management platform.

Ashfak Sutar - ITKARS AI CONSULTING (OPC) PRIVATE LIMITED | LinkedIn

HealthMate AI is an AI -driven digital healthcare platform that I designed and built to simplify patient access, improve clinical triage, and streamline healthcare operations through a unified, intelligent platform.

UK’s Flexzo AI raises €10.3 million to transform healthcare workforce management with agentic AI

Flexzo AI, a UK-based startup providing an agentic AI workforce system for healthcare providers, today announced the completion of a €10.3 million ($12 million) Series A funding round to support ongoing development and commercialization of its AI-powered workforce management platform.

Parallel: $20 Million Raised For AI Agents Automating Hospital Administration

Parallel, a Paris-based startup building AI agents for hospitals, has raised $20 million in Series A funding to accelerate the deployment of its technology across healthcare systems. The round was led by Index Ventures, with participation from Frst, YC, Hexa, and angel investors including Arthur Mensch of Mistral, Felix Blossier, and Quentin de Metz of Pennylane. The funding comes less than a year after the company’s $3.

Fitbit's AI Health Coach Will Soon Be Able to Read Your Medical Records

Google, which this week announced it is giving Fitbit's AI health coach the ability to read your medical records, is hoping the answer is yes, following rivals like Amazon, OpenAI, and Microsoft in betting big on AI-powered health care.

Flexzo AI Raises $12M

Flexzo AI, a London-based provider of an agentic AI workforce system for healthcare providers, raised $12 million in Series A funding.

Vital Wins Pinnacle Award for Transforming Hospital AI Communication

Vital has been named a Diamond winner in the 2026 Pinnacle Awards for its AI-driven patient experience platform that redefines hospital communication.

Follow the Rules (or Not): Community Norms and AI-Generated Support in Online Health Communities

Generative AI (GenAI) is increasingly being integrated into the online ecosystem, including online health communities (OHCs), where people with diverse health conditions exchange social support. For example, in OHCs, support providers are beginning to share content generated, directly or indirectly, by popular GenAI-based tools. OHCs are governed by norms that define appropriate behavior when providing support.

r/perplexity_ai on Reddit: Perplexity Computer now connects to your health apps, wearable devices, lab results, and medical records.

the biggest concern for healthcare industry to use AI is that AI hallucinates and because of that we cant guarantee about the result. so how are we tackling this with perplexity computer?

The Download: Quantum computing for health, and why the world doesn’t recycle more nuclear waste

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. A $5 million prize awaits proof that quantum computers can solve health care problems  In a laboratory on the outskirts of Oxford, a quantum computer built from atoms and light awaits…

A $5 million prize awaits proof that quantum computers can solve health care problems

I’m standing in front of a quantum computer built out of atoms and light at the UK’s National Quantum Computing Centre on the outskirts of Oxford. On a laboratory table, a complex matrix of mirrors and lenses surrounds a Rubik’s Cube–size cell where 100 cesium atoms are suspended in grid formation by a carefully manipulated…

Parallel Raises $20M

Y Combinator alum Parallel has secured a $20 million Series A funding to develop AI agents aimed at performing administrative tasks in hospitals, addressing inefficiencies in healthcare operations.

South Korea Launches AI-Led Brain Industry Project

South Korea’s Ministry of Science and ICT on Wednesday announced plans to promote a bold R&D project in future brain industries that brings together domestic capabilities in brain research, artificial intelligence, healthcare and advanced manufacturing.

F.D.A. Investigates 7 E. Coli Illnesses as Raw Dairy Farm Denies Any Link

The outbreak has sickened people in California, Texas and Florida. The agency said Cheddar cheese products from Raw Farm are “the likely source,” but the company denies it and has not recalled them.

The AI workforce is now ‘hirable’: How Nvidia is rewiring healthcare from the inside out

As an industry, healthcare tends to be slow-moving and significantly behind others. There are many reasons for this, including budgets, availability of technology and the fact that any errors in healthcare can result in lost lives. Healthcare transformation has been a big part of past Nvidia GTC conferences, and it was again this year.

Fitbit's AI Coach Improves Sleep Tracking, Can Soon Read Your Medical Records

Two major upgrades to Fitbit’s AI Coach aim to improve sleep insights and to use your medical records to inform its advice. Fitbit’s AI-powered Personal Health Coach is designed to give you more personalized guidance, and various new upgrades are coming to help improve sleep and better understand your medical history. Fitbit's AI Personal Health Coach is in a public preview on both Android and <a …

Barcelona’s Health Lean Analytics bags over €2.1 million to streamline hospital operations with AI and data analytics

Health Lean Analytics (HLA), a Barcelona-based startup specialising in automating data collection in hospital environments, has closed a financing round that brings the total capital raised to more than €2. 1 million.   The transaction involves a mix of private capital and public funding, with Novanta, a US-listed public company, serving as a technology and strategic partner.

Caging the Agents: A Zero Trust Security Architecture for Autonomous AI in Healthcare

Autonomous AI agents powered by large language models are being deployed in production with capabilities including shell execution, file system access, database queries, and multi-party communication. Recent red teaming research demonstrates that these agents exhibit critical vulnerabilities in realistic settings: unauthorized compliance with non-owner instructions, sensitive information disclosure, identity spoofing, cross-agent propagation of unsafe practices, and indirect prompt injection thr...

Ciaodott Raises €1.5M for AI-Powered Voice Assistant in Italian Healthcare

Ciaodott, an AI-powered voice assistant for the Italian healthcare sector, has raised €1.5 million in a pre-seed funding round led by Techshop, with participation from Vento, Club degli Investitori, Growth Engine, and Alpha Venture. Founded in 2025, the company addresses the need for more accessible and user-friendly healthcare services in Italy.

Conduit Health Raises $17M for AI-Powered Medicare Navigation

Conduit Health, based in New York, has secured a $17 million Series A funding round led by Drive Capital from Ohio. The company addresses the complex challenges Medicare and Medicaid patients face in accessing insurance-covered medical supplies.

Michał Nedoszytko MD, PhD - Executive SGH-WUM MBA w ochronie zdrowia | LinkedIn

Healthcare AI needs health professionals Healthcare AI doesn’t just need engineers. It needs health professionals.

Making Health Monitoring Simple: SheTech Interns Interview Mou Nandi

Monere (Lehi, UT) CEO Mou Nandi transforms anemia screening with a smartphone app, blending AI and accessibility. Her story highlights innovation, resilience, and inspiring guidance for young women pursuing STEM and health technology.

Oxford Medical Simulation Raises €5.78 Million

Oxford Medical Simulation (OMS), a UK startup, has raised €5. 78 million (£5 million) in growth funding from Salica Investments. The funding will enable OMS to expand into U.

Understood Care Raises $5M in Seed Funding

Understood Care, a New York City-based provider of an AI-native patient advocacy platform, raised $5 million in seed funding led by Rethink Education and Zeal Capital Partners. This funding follows a $3.4 million pre-seed round led by 1984 Ventures, with participation from Y Combinator.

Open Biomedical Knowledge Graphs at Scale: Construction, Federation, and AI Agent Access with Samyama Graph Database

Google scraps AI search feature that crowdsourced amateur medical advice

Exclusive: Revelation comes as company faces mounting scrutiny over use of AI to provide health tips Google has dropped a new artificial intelligence search feature that gave users crowdsourced health advice from amateurs around the world. The company had said its launch of “What People Suggest”, which provided tips from strangers, showed “the potential of AI to transform health outcomes across the globe”. Continue reading...

A Hybrid AI and Rule-Based Decision Support System for Disease Diagnosis and Management Using Labs

This research paper outlines the development and implementation of a novel Clinical Decision Support System (CDSS) that integrates AI predictive modeling with medical knowledge bases. It utilizes the quantifiable information elements in lab results for inferring likely diagnoses a patient might have. Subsequently, suggesting investigations to confirm the likely diagnoses -- an assistive tool for physicians.

Exploring Life-saving AI Tech

AI and 5G technologies problem-solving in emergency response and healthcare

Microsoft Unveils Copilot Health

Microsoft introduces Copilot Health, an AI tool integrating health records and wearable data to provide personalized medical insights while ensuring data privacy.

AI Chatbots' Diet Plans Fall Short

A new study reveals AI chatbots dangerously undercount calories in weight-loss meal plans for teens, potentially harming growth and health.

Can AI Help Predict Heart Failure?

Researchers at MIT, Mass General Brigham, and Harvard Medical School developed a deep-learning model to forecast a patient's heart failure prognosis up to a year in advance.

Translucent raises $27M to help healthcare firms automate financial visibility

Translucent AI Inc. , which provides financial visibility for hospitals and clinics, announced today it has raised $27 million in early-stage funding to bring its AI-native platform to healthcare organizations. Spending in healthcare was projected to hit around $5.

A.I. Chatbots Want Your Health Records. Tread Carefully.

Following rivals like Amazon and OpenAI, Microsoft is upgrading its artificially intelligent assistant to track your health. There are benefits and risks to consider.

Palantir’s NHS England contract ‘opens door to government abuse of power’, health bosses told

Health justice charity Medact says data-sharing potential could be used for UK version of US immigration raids Palantir’s NHS contract opens the door to the Big Brother-style data-sharing that Reform UK would use for a version of US immigration raids, health bosses have been told. Palantir Technologies – the data analytics company founded by Peter Thiel and Alex Karp – won a £330m NHS England contract to deliver the Federated Data Platform in 2023. Continue reading...

Evaluation format, not model capability, drives triage failure in the assessment of consumer health AI

Ramaswamy et al. reported in \textit{Nature Medicine} that ChatGPT Health under-triages 51. 6\% of emergencies, concluding that consumer-facing AI triage poses safety risks.

US Insurers and Hospitals Turn to AI

US insurers and hospitals are turning to artificial intelligence to settle claims and payments, with experts having a hard time predicting a winner in this tug-of-war.

Singapore Updates Guidelines On AI In Healthcare

Singapore has updated its Artificial Intelligence in Healthcare Guidelines to strengthen governance of AI use in healthcare, clarifying responsibilities across developers, healthcare institutions and clinicians while expanding guidance on transparency and risk management.

Conversational Diagnostic AI Shows Promise in Clinical Study

This article examines Google’s research into conversational diagnostic AI systems that assist with patient triage and medical evaluation using large language models in real clinical environments. Our analysts emphasized that this is one of the first structured real-world clinical studies evaluating a conversational AI system for healthcare diagnosis, with results suggesting performance comparable to clinicians in some triage tasks.

Microsoft Previews Copilot Health AI Tool

Microsoft today announced Copilot Health, a new AI service that lets users upload electronic health records and data from fitness trackers and other devices. Why it matters: Microsoft is entering one of AI's fastest-growing arenas — health care — as OpenAI, Amazon and others expand their medical chatbot offerings.

Albanian-Finnish startup Bliss raises €232.4k to build “AI that understands the cultural layer of mental health”

Bliss, a Finland-headquartered mental health startup, has raised €232. 4k ($270k) in angel funding to build what it calls “culturally intelligent” AI infrastructure for therapy. The angel funding was led by Keiretsu Forum, Finest Love VC, and Plug and Play.

3 Questions: Building Predictive Models to Characterize Tumor Progression

Researchers are working on building predictive models to characterize tumor progression. The models use machine learning algorithms to analyze data and make predictions about the behavior of tumors.

Amazon Launches Its Healthcare AI Assistant on Its Website and App

Health AI can answer questions, explain health records, manage prescription renewals, book appointments, and more.

A prospective clinical feasibility study of a conversational diagnostic AI in an ambulatory primary care clinic

Large language model (LLM)-based AI systems have shown promise for patient-facing diagnostic and management conversations in simulated settings. Translating these systems into clinical practice requires assessment in real-world workflows with rigorous safety oversight. We report a prospective, single-arm feasibility study of an LLM-based conversational AI, the Articulate Medical Intelligence Explorer (AMIE), conducting clinical history taking and presentation of potential diagnoses for patients ...

Semantic Risk Scoring of Aggregated Metrics: An AI-Driven Approach for Healthcare Data Governance

Large healthcare institutions typically operate multiple business intelligence (BI) teams segmented by domain, including clinical performance, fundraising, operations, and compliance. Due to HIPAA, FERPA, and IRB restrictions, these teams face challenges in sharing patient-level data needed for analytics. To mitigate this, A metric aggregation table is proposed, which is a precomputed, privacy-compliant summary.

AI Chatbots and Mental Health Crises

Claims that AI tools can reinforce delusions or push vulnerable users toward suicide are among the rare tech flashpoints that spark bipartisan alarm on Capitol Hill.

AI-Driven Health Tourism Revolution

Voiso and Doktor365 have teamed up to enhance health tourism through AI-powered contact centers and workflow tools, aiming to streamline patient journeys and improve satisfaction.

Isaac Cudjoe - Heartili | LinkedIn

It’s AI that creates more billable notes, tags diagnoses for better risk scores, and automates billing inputs at the point of care. The adoption of AI in healthcare has the potential to significantly improve patient outcomes and streamline clinical workflows.

AI Model Predicts Alzheimer's with 92.87% Accuracy

An AI model predicts Alzheimer's from MRI brain volume loss with 92.87% accuracy.

AWS Launches AI Agent Platform for Healthcare

AWS launches a new AI agent platform specifically for healthcare.

Ricoh's AI Document Processing Solution

Ricoh built a scalable intelligent document processing solution using Amazon Textract and Amazon Bedrock to classify, split, and extract information from large, complex healthcare documents in a regulated environment. The solution shows a practical Generative AI deployment with measurable business impact: onboarding dropped from 4–6 weeks to 2–3 days, engineering hours per deployment fell by more than 90%, and the platform is positioned to scale from roughly 10,000 to over 70,000 documents per month.

Singapore to Roll Out AI Health Screening

Singapore will roll out an artificial intelligence-based risk assessment tool for health screening under its Healthier SG program from early 2027.

India Launches SAHI for Ethical AI in Healthcare

India introduces SAHI, a comprehensive framework for ethical AI deployment in healthcare, emphasizing patient care, data diversity, and public trust.

India Launches SAHI for Ethical AI in Healthcare

India introduces SAHI, a comprehensive framework for ethical AI deployment in healthcare, emphasizing patient care, data diversity, and public trust. This initiative, supported by global collaboration, aims to integrate AI responsibly by enhancing governance, data infrastructure, and workforce readiness.

Amazon Launches AI-Enabled Platform to Automate Healthcare Administrative Tasks

Amazon's cloud unit launched an AI-enabled platform to automate healthcare administrative tasks and improve patient care.

CVS Taps Google Cloud to Roll Out AI-Powered Health Platform

CVS Health will launch an AI-enabled platform in partnership with Google Cloud to help customers manage their health in real-time.

RecovryAI's Virtual Care Assistants Earn FDA Breakthrough Designation

RecovryAI's Virtual Care Assistants have secured FDA Breakthrough Device Designation to enhance post-surgery recovery with AI-driven monitoring. Focused initially on joint arthroplasty, the device aims to reduce readmissions and improve patient outcomes, while the company anticipates regulatory submissions soon.

AI Chatbots in Healthcare Revolutionize Patient Interaction

AI chatbots, like OpenAI's ChatGPT Health, are revolutionizing health queries by analyzing medical data, though they lack the nuance of human interaction and professional oversight. Experts advise cautious use, highlighting risks of overconfidence and data privacy concerns, as these tools can provide plausible but incorrect medical advice.

OpenRad: a Curated Repository of Open-access AI models for Radiology

The rapid developments in artificial intelligence (AI) research in radiology have produced numerous models that are scattered across various platforms and sources, limiting discoverability, reproducibility and clinical translation. Herein, OpenRad was created, a curated, standardized, open-access repository that aggregates radiology AI models and providing details such as the availability of pretrained weights and interactive applications. Retrospective analysis of peer reviewed literature and p...

Artificial Intelligence: The Truths and Myths Behind the Platform.

The Massachusetts Institute of Technology reports that AI is least disruptive in Healthcare, Energy and what MIT calls “Advanced Industries.” These industries it should be noted employ many skilled workers, such as physicians and clinical ...

As AI enters the operating room, reports arise of botched surgeries and misidentified body parts | Reuters

Insight into the incidents comes as AI is beginning to transform the world of health care. Proponents predict the new technology will help find cures for rare diseases, discover new drugs, enhance surgeons’ skill and empower patients.

Doctolib Accelerates Developer Productivity

Doctolib, a large European healthcare platform, uses Claude Code to streamline developer onboarding and reduce engineering bottlenecks. This case study provides a high-level example of AI-driven DevOps and agentic software development in healthcare.

Doctolib Accelerates Developer Productivity

Doctolib, a large European healthcare platform, uses Claude Code to streamline developer onboarding and reduce engineering bottlenecks. This case study provides a high-level example of AI-driven DevOp

Enterprise AI Surge: Governance-Driven Prompt Engineering Transforms Workflows Across Sectors

Enterprise AI growth is fueled by the shift to governance-driven prompt engineering and integration into SaaS, with major tech firms advancing large language models. The healthcare sector led market share in 2025, while finance is set for rapid growth, emphasizing chatbots and virtual assistants in customer service.

This job has become the ultimate case study for why AI won’t replace human workers

Want to understand how artificial intelligence could change your job? Look to radiology as a clue. Radiology has become a recent talking point in the AI...

AI in 2026: The data drought, healthcare opportunities, and space tech

In the last year, we’ve seen huge strides in AI development and adoption. Whilst staggering valuations take the headlines, I’m interested in the coming data drought, opportunities in healthcare, and space tech. These are the industries where we expect the most impact to be achieved.

Oura Launches AI-Powered Women's Health Advisor

Oura introduces the Oura Advisor, an AI model offering personalized women's health insights from puberty to menopause using data from the Oura Ring. Available for testing through Oura Labs, it focuses on educating users and enhancing dialogues with healthcare providers while maintaining strict privacy controls.

With half of chronically ill patients failing to take medication correctly, Oska Health secures €11 million for personal health app

Frankfurt-based Oska Health, a hybrid care provider combining personal health coaches with AI, has raised €11 million in Seed funding to scale its continuous care model for high-risk, chronically ill patients. The round is led by Capricorn Partners and SwissHealth Ventures, with participation from Revent, Calm Storm, LBBW Venture Capital, BMH, GoHub Ventures, and Aurum […] The post With half of chronically ill patients failing to take medication correctly, Oska Health secures €11 million for personal health app appeared first on EU-Startups.

TripleKey Appoints Former Health System CIO Jon Brown As Chief Information Officer

TripleKey, a healthcare-focused software risk and compliance platform, announced the appointment of Jon Brown as Chief Information Officer. Brown, a former healthcare Chief Information Officer and longtime healthcare technology executive, joins the company as health systems become increasingly software-driven and face accelerating change fueled by AI-assisted development. The post TripleKey Appoints Former Health System CIO Jon Brown As Chief Information Officer appeared first on Pulse 2.

Pranjal Sharma - SkinSense AI LLP | LinkedIn

Aspiring technopreneur and B.Tech undergraduate in Data Science &amp; Artificial Intelligence… · Experience: SkinSense AI LLP · Education: IIIT-Naya Raipur · Location: India · 500+ connections on LinkedIn. View Pranjal Sharma’s profile on LinkedIn, a professional community of 1 billion members.