TY - JOUR
T1 - Generative Artificial Intelligence Through ChatGPT and Other Large Language Models in Ophthalmology
T2 - Clinical Applications and Challenges
AU - Tan, Ting Fang
AU - Thirunavukarasu, Arun James
AU - Campbell, J. Peter
AU - Keane, Pearse A.
AU - Pasquale, Louis R.
AU - Abramoff, Michael D.
AU - Kalpathy-Cramer, Jayashree
AU - Lum, Flora
AU - Kim, Judy E.
AU - Baxter, Sally L.
AU - Ting, Daniel Shu Wei
N1 - Publisher Copyright:
© 2023 American Academy of Ophthalmology
PY - 2023/12
Y1 - 2023/12
N2 - The rapid progress of large language models (LLMs) driving generative artificial intelligence applications heralds the potential of opportunities in health care. We conducted a review up to April 2023 on Google Scholar, Embase, MEDLINE, and Scopus using the following terms: “large language models,” “generative artificial intelligence,” “ophthalmology,” “ChatGPT,” and “eye,” based on relevance to this review. From a clinical viewpoint specific to ophthalmologists, we explore from the different stakeholders’ perspectives—including patients, physicians, and policymakers—the potential LLM applications in education, research, and clinical domains specific to ophthalmology. We also highlight the foreseeable challenges of LLM implementation into clinical practice, including the concerns of accuracy, interpretability, perpetuating bias, and data security. As LLMs continue to mature, it is essential for stakeholders to jointly establish standards for best practices to safeguard patient safety. Financial Disclosure(s): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
AB - The rapid progress of large language models (LLMs) driving generative artificial intelligence applications heralds the potential of opportunities in health care. We conducted a review up to April 2023 on Google Scholar, Embase, MEDLINE, and Scopus using the following terms: “large language models,” “generative artificial intelligence,” “ophthalmology,” “ChatGPT,” and “eye,” based on relevance to this review. From a clinical viewpoint specific to ophthalmologists, we explore from the different stakeholders’ perspectives—including patients, physicians, and policymakers—the potential LLM applications in education, research, and clinical domains specific to ophthalmology. We also highlight the foreseeable challenges of LLM implementation into clinical practice, including the concerns of accuracy, interpretability, perpetuating bias, and data security. As LLMs continue to mature, it is essential for stakeholders to jointly establish standards for best practices to safeguard patient safety. Financial Disclosure(s): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
KW - Artificial intelligence
KW - ChatGPT
KW - Chatbots
KW - Large language models
UR - http://www.scopus.com/inward/record.url?scp=85174553744&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85174553744&partnerID=8YFLogxK
U2 - 10.1016/j.xops.2023.100394
DO - 10.1016/j.xops.2023.100394
M3 - Article
AN - SCOPUS:85174553744
SN - 2666-9145
VL - 3
JO - Ophthalmology Science
JF - Ophthalmology Science
IS - 4
M1 - 100394
ER -