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Reinforcement learning in ophthalmology: potential applications and challenges to implementation

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). Green open access

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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.

Type: Article
Title: Reinforcement learning in ophthalmology: potential applications and challenges to implementation
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/S2589-7500(22)00128-5
Publisher version: https://doi.org/10.1016/S2589-7500(22)00128-5
Language: English
Additional information: © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/).
UCL classification: UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Institute of Ophthalmology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL
URI: https://discovery.ucl.ac.uk/id/eprint/10153350
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