Healthcare
Case Studies
Newsfor Healthcare
AI system predicts risk of diabetes with an 88% accuracy rate in tests
Shanghai’s Ruijin Hospital has partnered with the Chinese AI startup 4Paradigm to apply AI in healthcare, particularly chronic health conditions. They have tested an AI-backed diabetes prediction and management product, which they hope will help them identify patients at risk of developing diabetes up to 15 years in advance. The system showed an 88% accuracy in tests on information from 170,000 people.
Linked use case: Predict risk of condition developing at an early stage
Action Against AMD and Benevolent AI aim to find treatments for age-related macular degeneration (AMD) that causes blindness using machine learning
Benevolent AI and a group of four charities, Blind Veterans UK, Fight for Sight, the Macular Society and Scottish War Blinded, have partnered to find treatments and a potential cure for AMD. Benevolent AI''s machine learning technology will be leveraged to analyse existing scientific papers, clinical trials information, images, formulas, patents and any other knowledge we have on age-related macular degeneration (AMD) to uncover potential patterns, connections and point researchers towards important research areas.
Linked use case: Accelerate drug discovery by automating research stages and data integration and analysis
Advisory committee to the National Institutes of Health to identify health needs in search query data in the US using machine learning
A 12-member advisory body to the National Institutes of Health, ACD Working Group on AI including researcher Rediet Abebe, is attempting to analyse search engine data in the US to identify health needs using machine learning. The group of researchers and scientists is to give their final recommendations to NIH director Francis Collins in December 2019, while they will present their interim findings in June.
Linked use case: Search and capture knowledge from the world wide web
Amazon announces its Comprehend Medical platform to analyse unstructured medical texts
Amazon has announced a machine learning service for analysing and extracting information from different unstructured medical records and text. Comprehend Medical is accessible for developers through AWS, and is partnering with healthcare research centres to assist with things like clinical trial patient matching.
Linked use case: Optimise clinical trial design including patient selection
Anglia Ruskin University researchers develop mobile system which detects tuberculosis with 98.4% accuracy
Researchers from Anglia Ruskin University test different machine learning methods to classify digital images created using biosensors for the presence of tuberculosis antibodies. The goal was to create a system which could process and classify the images on a portable phone. They were able to achieve a 98.4% accuracy doing so using a random forest method.
Linked use case: Diagnose known diseases from scans, images, biopsies, audio and other data
Anthem aims to predict the occurrence of allergies using machine learning
Anthem has partnered with Doc.ai to execute a 12-month trial to analyse how allergies affect people. The system is based on AI and machine learning algorithms to identify predictive models and identify patterns and on blockchain to ensure the privacy and anonymity of medical data.
Linked use case: Predict personalised health outcomes to recommend individual treatment approach
Aravind Eye Hospital identifies eye complications arising from diabetes with a 97.5% accuracy using machine learning
Dr Ramasamy Kim and his team at the Aravind Eye Hospital in Madurai have developed a system capable of identifying eye complications arising from diabetes. Over the past five years, the doctors have been training the system by examining 15,000 retinal images and indicating spots or other abnormal features. Based on that the system has achieved a 97.5% accuracy, in testing, in identifying such complications. The hospital has been in a pilot project to check the system against human assessment since 2016.
Linked use case: Diagnose known diseases from scans, images, biopsies, audio and other data
Assistance publique – Hôpitaux de Paris optimises staffing by predicting emergency room admission rates by hour and day using machine learning
Using TAP the open source AI platform powered by Intel to analyse data from internal and external sources such as hospital admissions records, emergency arrival rates can be predicted accurately. This has led to better staffing and resource allocation at the hospital.
Linked use case: Predict potential staffing requirements to optimise resourcing
Autonomous Healthcare detects different types of ventilator asynchrony in ICU patients with machine learning
Autonomous Healthcare has developed technology that can manage a patient''s ventilation in the ICU. Using machine learning, it is able to detect anomalies between the patient''s own and a mechanical ventilator''s inhalation and exhalation patterns. The system was trained on data of waveforms from patients on ventilators and learned the signatures of different asynchrony types. In its first assessment on data it achieved the same accuracy as human doctors, and it is now being tested with real patients.
Linked use case: Alerting and diagnostics from real time patient data
Babylon Health claims 82% accuracy for video medical diagnosis based on machine learning and natural language processing
Babylon health has developed an inference engine based on machine learning to diagnose disease. It refers to the knowledge graph built from medical knowledge. The patient interface is an app with video capabilities which uses natural language processing. Babylon has a partnership with the NHS in the UK and is being piloted in Rwanda. Performance and data privacy concerns have been raised.
Linked use case: Provide first line of medical advice online through chatbot
Banner Health saves $29m in three years by helping avoid hospitalisation for patients with multiple chronic conditions by remote monitoring of vitals and analysis with machine learning
Banner Health has implemented Philip''s tele-health platform and predictive analytics to manage patients with multiple chronic conditions. Patients are monitored using wearables in real-time and changes in vitals that lead to hospitalisation are identified automatically using machine learning and preventive measures are suggested. Communication with patients to encourage medication adherence and self-care are done using chatbots.
Linked use case: Predict personalised health outcomes to recommend individual treatment approach
Bayer aims to spot drug-associated side effects earlier with the use of machine learning, RPA and natural language processing
Bayer has partnered with Genpact to leverage artificial intelligence for its effort to process adverse events and spot drug-associated side effects earlier. The vendor is providing a solution that is able to optimise Bayer''s pharmacovigilinace practice for consumer health and pharmaceuticals. The Pharmacovigilance Artificial Intelligence (PVAI) solution works by automatically drawing AE data from unstructured and semi-structured source documents with the use of optical character recognition, robotic process automation, natural language processing, and machine learning.
Linked use case: Reduce side effects by collating patient data and optimising processes
Beijing Tian Tan Hospital is testing the detection of type, location and severity of a stroke using machine learning
Beijing Tian Tan Hospital is testing the detection of type, location and severity of a stroke using a machine learning from Infervision. Accurately estimating blood loss and location of a stroke is critical to determining treatment. Time is critical due to brain cell death after a stroke. Training deep neural networks on CT images of brain stroke patients allows diagnosis to be made more quickly and accurately.
Linked use case: Diagnose known diseases from scans, images, biopsies, audio and other data
Biogen to design individualized treatment plans for newly diagnosed patients with providers using machine learning
Prognos has built a vast database of diseases and treatments including laboratory results and clinical diagnostic data. Biogen uses the database and machine learning to identify effective treatment plans for newly diagnosed patients enhancing treatment decision-making, risk management, and quality improvement.
Linked use case: Predict personalised health outcomes to recommend individual treatment approach
Blue Cross Blue Shield predicts individual propensity for opioid abuse with 85% accuracy to modify insurance pricing and support appropriate interventions using machine learning analytics
Blue Cross Blue Shield maps risk factors associated with the potential of opioid abuse among patients. It is using data such as demographic, location and frequency of pharmacy claims data to build models to predict individual propensity of abuse. They can then stage interventions or use nudges to prevent the behaviour. It is using machine learning predictive analytics.
Linked use case: Predict personalised health outcomes to recommend individual treatment approach
Blue Cross Blue Shield reduced post hospitalisation costs by over 20% by driving patient engagement with digital post care programs using smart devices, sensors and machine learning
Blue Cross and Blue Shield of Nebraska (BCBSNE) started using Wellframe''s solution to help customers manage health conditions. Using analytics and mobile app, they were able to implement health coaching and behavioral health programs. The app uses machine learning to map how to connect to customers better. It reduced telephone care manager support costs by 17% and mobile care management costs by 41%.
Linked use case: Detect potential medical events from wearable sensor data and signal emergency response
BlueDot identified Wuhan pneumonia outbreak from social media posts before WHO made public announcement on COVID-19
BlueDot picked up on a cluster of “unusual pneumonia” cases happening near a market in Wuhan, China, and flagged it. This would become better known as the epicentre of what would come to be known as COVID-19. This was nine days before the World Health Organization released its statement alerting people to the emergence of a novel coronavirus in China.
Linked use case: Track and predict disease vector in general population
Capitol Health improves accuracy of diagnosis from scans, X-rays etc using deep learning
Capitol Health, a diagnostic imaging services provider, has partnered with Enlitic to develop a deep learning system to assess the condition of patient and thus improve outcomes. Capitol Health is building a database mapping the entire human body. Some of the projects include automating detection of lung cancer nodules from chest CT images, extremity (e.g. wrist) bone fractures etc. Diagnosis is upto 50% more accurate than trained radiologists.
Linked use case: Identify disease via biopsy
Cardiogram detects atrial fibrillation with 97% accuracy surpassing FDA-cleared wearable ECG devices using the Apple Watch and machine learning
Atrial fibrillation, the most common abnormal heart rhythm, causes 1 in 4 strokes and frequently goes undiagnosed. In the clinically validated mRhythm study by Cardiogram and the University of California San Fransisco (UCSF), DeepHeart, which is a a semi-supervised deep neural network, detected atrial fibrillation with 97% accuracy (c-statistic) in a hospital environment, using optical heart rate sensors, setting the stage for cost-effective, broadly-deployed AF screening.
Linked use case: Detect potential medical events from wearable sensor data and signal emergency response
Centers for Disease Control and Prevention reduce polio report generation time to 1 hour using machine learning to automate regional mapping of the disease
The Centers for Disease Control and Prevention investigate evolving strands of the polio virus and map its activity dgeographically. Using machine learning has reduced the manual tie involved in generating reports to 1 hour and from clustering virus strains from over 3 months to 1 week.
Linked use case: Track and predict disease vector in general population
Children''s Hospital of Los Angeles predicts when to discharge patients from pediatric intensive care using deep recurrent neural networks
Children''s Hospital of Los Angeles analysed the history of 5,500 patient vitals from pediatric intensive care unit (ICU) episodes to better predict when to discharge. They build four predictive models from basic regression to recurrent neural networks and found the deep neural networks performed well.
Linked use case: Predict physiologically acceptable states for discharge from Pediatric Intensive Care Unit
Cigna to identify at-risk patients from laboratory results and clinical diagnostic data, and offer early treatment options using machine learning
Using AI to analyze its vast database of clinical data including lab results, diagnosis, scans etc, Prognos can identify patients who are at-risk. Cigna is planning to offer high risk patients customized healthcare plans as well as early consultation or diagnostic services
Linked use case: Predict personalised health outcomes to recommend individual treatment approach
Cincinnati Children’s Hospital Medical Centre predicted at expert-level 91% accuracy which students are at higher risk of perpetrating school violence using machine learning on interview scores
A pilot study indicates that AI may be useful in predicting which students are at higher risk of perpetrating school violence. At 91% accuracy it is as accurate as a team of child and adolescent psychiatrists, including a forensic psychiatrist.
Linked use case: Predict likelihood of recividism or criminal activity on an individual basis
Cleo offers personalised support system for expecting parents which maps user preferences and helps them find matching providers and other services using machine learning
Cleo uses machine learning to personalise support offered to expecting parents. They match user preference with their network of health care providers and as the user interacts more with the app it finds better matching services. Cleo is used by several companies like Pinterest, Reddit, Slack etc to improve employee retention rates.
Linked use case: Monitor and advise on key health indicators during pregnancy
Cleveland Clinic and Microsoft identify at-risk patients in ICU to prevent the occurrence of cardiac failure with machine learning ensembles
Cleveland Clinic worked with Microsoft to develop predictive models to identify at-risk patients of cardiac failure in ICU. Analysing vital data collected from ICU along with lab test and patient information, they developed Boosted Decision Tree models. They believe the predictive model will allow for timely intervention by medics.
Linked use case: Alerting and diagnostics from real time patient data