Machine learning leverages statistical and computer science principles to develop algorithms capable of improving performance through interpretation of data rather than through explicit instructions. Alongside widespread use in image recognition, language processing, and data mining, machine learning techniques have received increasing attention in medical applications, ranging from automated imaging analysis to disease forecasting. This review examines the parallel progress made in epilepsy, highlighting applications in automated seizure detection from electroencephalography (EEG), video, and kinetic data, automated imaging analysis and pre‐surgical planning, prediction of medication response, and prediction of medical and surgical outcomes using a wide variety of data sources. A brief overview of commonly used machine learning approaches, as well as challenges in further application of machine learning techniques in epilepsy, is also presented. With increasing computational capabilities, availability of effective machine learning algorithms, and accumulation of larger datasets, clinicians and researchers will increasingly benefit from familiarity with these techniques and the significant progress already made in their application in epilepsy.

We are probably still quite some way off from seeing Artificial Intelligence (AI) replace doctors, but there are already lots of proven use-cases where AI is being used to augment the medical profession. One proven area is in medical imaging where AI and computer vision is helping with medical scan and imaging analysis to help support radiologists and other clinicians. One startup operating in this space is AIdoc Medical. The company has built what co-founder and CEO Elad Walach describes as an AI that can spot visual abnormalities in medical scans. The technology is designed to fit into a radiologist's existing workflow to help make their job more efficient.