TY - JOUR
T1 - Large language models and their impact in ophthalmology
AU - Betzler, Bjorn Kaijun
AU - Chen, Haichao
AU - Cheng, Ching Yu
AU - Lee, Cecilia S.
AU - Ning, Guochen
AU - Song, Su Jeong
AU - Lee, Aaron Y.
AU - Kawasaki, Ryo
AU - van Wijngaarden, Peter
AU - Grzybowski, Andrzej
AU - He, Mingguang
AU - Li, Dawei
AU - Ran Ran, An
AU - Ting, Daniel Shu Wei
AU - Teo, Kelvin
AU - Ruamviboonsuk, Paisan
AU - Sivaprasad, Sobha
AU - Chaudhary, Varun
AU - Tadayoni, Ramin
AU - Wang, Xiaofei
AU - Cheung, Carol Y.
AU - Zheng, Yingfeng
AU - Wang, Ya Xing
AU - Tham, Yih Chung
AU - Wong, Tien Yin
N1 - Publisher Copyright:
© 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license
PY - 2023/12
Y1 - 2023/12
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85177769213&partnerID=8YFLogxK
U2 - 10.1016/S2589-7500(23)00201-7
DO - 10.1016/S2589-7500(23)00201-7
M3 - Review article
C2 - 38000875
AN - SCOPUS:85177769213
SN - 2589-7500
VL - 5
SP - e917-e924
JO - The Lancet Digital Health
JF - The Lancet Digital Health
IS - 12
ER -