Airbus used deep machine vision to detect clouds in satellites decreasing the error rate from 11% to 3%

Researchers from the University of California San Diego trained gliders to autonomously navigate atmospheric thermals with reinforcement learning

The US Department of Defense aims at increasing drone effectiveness by analysing collected footage with machine vision

Cambridge researchers develop a system to analyse cancer research papers to discover previously unexplored molecular biology links

DeepMind develops a highly accurate machine learning method for predicting protein structures

Healx's scientist predicts effectiveness of combinations of antibiotics using machine learning

MIT scientists develop system that crowdsources data to speed up drug discovery using neural networks

Researchers at Stanford University develop an approach for modeling polypharmacy side effects with graph convolutional networks outperforming baselines by up to 69%

Researchers at University of Lisbon accelerate drug discovery with the use of machine learning

Roche plans to reduce time-to-market for oncology medicine by streamlining clinical trial process with machine learning

Takeda partners with Numerate to improve pharmaceutical clinical trial efficiency by using AI to inform design decisions

University of Glasgow researchers predict virus reservoir hosts with 83.5% accuracy and provide hypotheses about unknown viral vectors

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

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

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

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

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

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

King's College London researchers automate real-time triage of X-ray radiograph reports with high accuracy

MIT researchers propose an efficient and accurate system for protecting privacy in healthcare datasets

PLA General Hospital in Beijing is using an algorithm for predicting if patients will wake up from a coma

Qure.ai can detect critical head trauma or stroke symptoms from CT scans with more than 95% accuracy using deep neural networks and natural language processing

Researchers at Osaka University differentiate between different types of cancer cells using a convolutional neural network