Volume 2 Issue 10, October 2018

A computational model of the heart that finds the optimal ablation sites to treat infarct-related ventricular tachycardia eliminates the need for invasive electrical mapping.

A deep-learning algorithm enables the real-time video-based recognition of polyps during colonoscopy, with sensitivities and specificities surpassing 90%.

Machine learning can predict and help interpret the risk of hypoxaemia during general anaesthesia.

Computational modelling, informed by data from 3D volumetric imaging of transparent and intact whole-tumour samples, predicts blood flow and the spatial distribution of drug uptake in tumours.

This Review summarizes the medical applications of artificial intelligence, and its economic, legal and social implications for healthcare.

A personalized virtual-heart model that determines optimal radio-frequency ablation targets for infarct-related tachycardia is validated in retrospective large-animal and patient studies, and in a prospective study in patients.

A deep-learning algorithm can detect polyps in the colon in real time and with high sensitivity and specificity, according to validation studies with prospectively collected images and videos from colonoscopies performed in 1,138 patients.

An alert system based on machine learning and trained on surgical data from electronic medical records helps anaesthesiologists prevent hypoxaemia during surgery by providing interpretable real-time predictions.

An assay that uses machine-learning algorithms on phenotypic-biomarker data from live primary cells predicts post-surgical adverse pathology in prostate-cancer and breast cancer tissue samples from patients.

The combination of mathematical modelling of tumour tissue, optical imaging of cleared tumours from animal models, and in vivo imaging of vascular perfusion in tumours predicts the tumour uptake and distribution of specific therapeutic agents.