Large language models and their impact in ophthalmology

Bjorn Kaijun Betzler, Haichao Chen, Ching Yu Cheng, Cecilia S. Lee, Guochen Ning, Su Jeong Song, Aaron Y. Lee, Ryo Kawasaki, Peter van Wijngaarden, Andrzej Grzybowski, Mingguang He, Dawei Li, An Ran Ran, Daniel Shu Wei Ting, Kelvin Teo, Paisan Ruamviboonsuk, Sobha Sivaprasad, Varun Chaudhary, Ramin Tadayoni, Xiaofei WangCarol Y. Cheung, Yingfeng Zheng, Ya Xing Wang, Yih Chung Tham, Tien Yin Wong

Research output: Journal article publicationReview articleAcademic researchpeer-review

29 Citations (Scopus)

Abstract

The advent of generative artificial intelligence and large language models has ushered in transformative applications within medicine. Specifically in ophthalmology, large language models offer unique opportunities to revolutionise digital eye care, address clinical workflow inefficiencies, and enhance patient experiences across diverse global eye care landscapes. Yet alongside these prospects lie tangible and ethical challenges, encompassing data privacy, security, and the intricacies of embedding large language models into clinical routines. This Viewpoint highlights the promising applications of large language models in ophthalmology, while weighing up the practical and ethical barriers towards their real-world implementation. This Viewpoint seeks to stimulate broader discourse on the potential of large language models in ophthalmology and to galvanise both clinicians and researchers into tackling the prevailing challenges and optimising the benefits of large language models while curtailing the associated risks.
Original languageEnglish
Pages (from-to)e917-e924
Number of pages8
JournalThe Lancet Digital Health
Volume5
Issue number12
DOIs
Publication statusPublished - Dec 2023

ASJC Scopus subject areas

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

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