Nath, Siddharth; Korot, Edward; Fu, Dun Jack; Zhang, Gongyu; Mishra, Kapil; Lee, Aaron Y; Keane, Pearse A; (2022) Reinforcement learning in ophthalmology: potential applications and challenges to implementation. The Lancet Digital Health 10.1016/S2589-7500(22)00128-5. (In press).

Preview

Text 1-s2.0-S2589750022001285-main.pdf - Published Version Download (925kB) | Preview

Abstract

Reinforcement learning is a subtype of machine learning in which a virtual agent, functioning within a set of predefined rules, aims to maximise a specified outcome or reward. This agent can consider multiple variables and many parallel actions at once to optimise its reward, thereby solving complex, sequential problems. Clinical decision making requires physicians to optimise patient outcomes within a set practice framework and, thus, presents considerable opportunity for the implementation of reinforcement learning-driven solutions. We provide an overview of reinforcement learning, and focus on potential applications within ophthalmology. We also explore the challenges associated with development and implementation of reinforcement learning solutions and discuss possible approaches to address them.

Download activity - last month

Export as JSONCSVXML

Download activity - last 12 months

Export as XMLJSONCSV

Downloads by country - last 12 months

Export as XMLJSONCSV

Archive Staff Only

View Item