Reinforcement learning in ophthalmology: potential applications and challenges to implementation

Siddharth Nath, Edward Korot, Dun Jack Fu, Gongyu Zhang, Kapil Mishra, Aaron Y. Lee, Pearse A. Keane

Research output: Contribution to journalReview articlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)e692-e697
JournalThe Lancet Digital Health
Issue number9
StatePublished - Sep 2022
Externally publishedYes

ASJC Scopus subject areas

  • Decision Sciences (miscellaneous)
  • Health Information Management
  • Health Informatics
  • Medicine (miscellaneous)


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