Alerting and diagnostics from real time patient data

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

Kaiser Permanente predicts probability of rapid deterioration of patient condition and alerts physicians using machine learning

Graphnet detects changes indicative of a seizure in epileptic patients using wearable tracker and machine learning and notifies the patient and carer via app

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

The European Emergency Number Association (EENA) to pilot heart attack detection service with the use of neural networks

El Camino Hospital reduces number of patient falls by 39% using machine learning to predict when a patient is about to fall

Japan to automate patient information entry and some medical procedures by investing in 10 AI-powered hospitals

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