TY - JOUR
T1 - Reinforcement learning in ophthalmology
T2 - potential applications and challenges to implementation
AU - Nath, Siddharth
AU - Korot, Edward
AU - Fu, Dun Jack
AU - Zhang, Gongyu
AU - Mishra, Kapil
AU - Lee, Aaron Y.
AU - Keane, Pearse A.
N1 - Publisher Copyright:
© 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license
PY - 2022/9
Y1 - 2022/9
N2 - 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.
AB - 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.
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U2 - 10.1016/S2589-7500(22)00128-5
DO - 10.1016/S2589-7500(22)00128-5
M3 - Review article
C2 - 35906132
AN - SCOPUS:85136397679
SN - 2589-7500
VL - 4
SP - e692-e697
JO - The Lancet Digital Health
JF - The Lancet Digital Health
IS - 9
ER -