Mayo Clinic researchers accurately diagnose hyperkalemia using a smartphone electrocardiogram device

Mercy Hospital Fort Smith improves patient flow and throughput in the ER to improves LWBS rates by over 30% using machine learning

NCI designated cancer centers reduces time to treatment for diagnosed patients by 5 days using machine learning and natural language processing

NHS investigates whether inflammation plays a role in mental illnesses by extracting blood test results from unstructured medical records with advanced natural language processing

Researchers develop a model to provide individualised and clinically interpretable treatment decisions to improve sepsis patients outcomes

Sheba Medical Center ensures accuracy of prescriptions and eliminates human error using machine learning

St. Michael’s Hospital predicts onset of sepsis among ICU patients with 90% accuracy using learning evolutionary algorithm framework

Suramericana optimises urgent operations scheduling with predictive analytics and machine learning

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

AI system predicts risk of diabetes with an 88% accuracy rate in tests

Action Against AMD and Benevolent AI aim to find treatments for age-related macular degeneration (AMD) that causes blindness using machine learning

Anthem aims to predict the occurrence of allergies using machine learning

Aravind Eye Hospital identifies eye complications arising from diabetes with a 97.5% accuracy using machine learning

Autonomous Healthcare detects different types of ventilator asynchrony in ICU patients with machine learning

Bayer aims to spot drug-associated side effects earlier with the use of machine learning, RPA and natural language processing

Capitol Health improves accuracy of diagnosis from scans, X-rays etc using deep learning

Cardiogram detects atrial fibrillation with 97% accuracy surpassing FDA-cleared wearable ECG devices using the Apple Watch and machine learning

Children's Hospital of Los Angeles predicts when to discharge patients from pediatric intensive care using deep recurrent neural networks

Cleveland Clinic and Microsoft identify at-risk patients in ICU to prevent the occurrence of cardiac failure with machine learning ensembles

Dana Farber Cancer Institute accelerates clinical trial recruitment by using machine learning for genome mapping to identify best candidates

Duke University is researching how to predict the onset of Parkinsons by analysing mouse usage with machine learning classifiers

Duke University is researching how to predict the onset of neurological disorders such as Parkinsons by analysing Microsoft-Bing search logs using machine-learned classifiers

Enlicit classifies malignant tumours with 50% better accuracy than humans and 0 false-negatives with deep learning

Enlitic augments radiologists to achieve 21% faster, 11% more sensitive and 9% more specific reading of fractures in X-rays with deep learning

Fraunhofer Heinrich Hertz Institute and University Medical Center Schleswig-Holstein, a German University Hospital, has developed an algorithm to detect the probability of getting heart attack from ECG and EEG, matching accuracies achieved by cardiologists

Google Health improves predictions of hospital patient medical outcomes by using deep neural networks trained on 46 billion data points

Google's Verily prioritizes patients by diagnosing retinopathy more accurately than ophthalmologists using deep neural networks

Huashan Hospital develops a robotics based radiosurgery procedure to treat inoperable intracranial tumour sizes achieving 55% reduction on average

Imperial College Researchers diagnose ovarian cancer patients with survival rates under 2 years

KenSci improves on prediction models of mortality risk six months to one year out through deep machine learning leading to better palliative care