Reinforcement learning in ophthalmology: potential applications and challenges to implementation. Issue 9 (September 2022)

• Record Type: Journal Article
• Authors: Nath, Siddharth; Korot, Edward; Fu, Dun Jack; Zhang, Gongyu; Mishra, Kapil; Lee, Aaron Y; Keane, Pearse A
• Is Part Of: Lancet. Volume 4:Issue 9(2022)
• Page Start: e692
• Page End: e697
• Publication Date: 2022-09
• Subjects: Medical care -- Data processing -- Periodicals; Medical care -- Information technology -- Periodicals; Medical informatics -- Periodicals; 610.285
• DOI: 10.1016/S2589-7500(22)00128-5
• Languages: English
• ISSNs: 2589-7500

Abstract

Summary: 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.