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Generative Artificial Intelligence Through ChatGPT and Other Large Language Models in Ophthalmology: Clinical Applications and Challenges

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,”...

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Autores principales: Tan, Ting Fang, Thirunavukarasu, Arun James, Campbell, J. Peter, Keane, Pearse A., Pasquale, Louis R., Abramoff, Michael D., Kalpathy-Cramer, Jayashree, Lum, Flora, Kim, Judy E., Baxter, Sally L., Ting, Daniel Shu Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10598525/
https://www.ncbi.nlm.nih.gov/pubmed/37885755
http://dx.doi.org/10.1016/j.xops.2023.100394
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author Tan, Ting Fang
Thirunavukarasu, Arun James
Campbell, J. Peter
Keane, Pearse A.
Pasquale, Louis R.
Abramoff, Michael D.
Kalpathy-Cramer, Jayashree
Lum, Flora
Kim, Judy E.
Baxter, Sally L.
Ting, Daniel Shu Wei
author_facet Tan, Ting Fang
Thirunavukarasu, Arun James
Campbell, J. Peter
Keane, Pearse A.
Pasquale, Louis R.
Abramoff, Michael D.
Kalpathy-Cramer, Jayashree
Lum, Flora
Kim, Judy E.
Baxter, Sally L.
Ting, Daniel Shu Wei
author_sort Tan, Ting Fang
collection PubMed
description 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.
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spelling pubmed-105985252023-10-26 Generative Artificial Intelligence Through ChatGPT and Other Large Language Models in Ophthalmology: Clinical Applications and Challenges Tan, Ting Fang Thirunavukarasu, Arun James Campbell, J. Peter Keane, Pearse A. Pasquale, Louis R. Abramoff, Michael D. Kalpathy-Cramer, Jayashree Lum, Flora Kim, Judy E. Baxter, Sally L. Ting, Daniel Shu Wei Ophthalmol Sci Original Article 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. Elsevier 2023-09-09 /pmc/articles/PMC10598525/ /pubmed/37885755 http://dx.doi.org/10.1016/j.xops.2023.100394 Text en © 2023 by the American Academy of Ophthalmology. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Original Article
Tan, Ting Fang
Thirunavukarasu, Arun James
Campbell, J. Peter
Keane, Pearse A.
Pasquale, Louis R.
Abramoff, Michael D.
Kalpathy-Cramer, Jayashree
Lum, Flora
Kim, Judy E.
Baxter, Sally L.
Ting, Daniel Shu Wei
Generative Artificial Intelligence Through ChatGPT and Other Large Language Models in Ophthalmology: Clinical Applications and Challenges
title Generative Artificial Intelligence Through ChatGPT and Other Large Language Models in Ophthalmology: Clinical Applications and Challenges
title_full Generative Artificial Intelligence Through ChatGPT and Other Large Language Models in Ophthalmology: Clinical Applications and Challenges
title_fullStr Generative Artificial Intelligence Through ChatGPT and Other Large Language Models in Ophthalmology: Clinical Applications and Challenges
title_full_unstemmed Generative Artificial Intelligence Through ChatGPT and Other Large Language Models in Ophthalmology: Clinical Applications and Challenges
title_short Generative Artificial Intelligence Through ChatGPT and Other Large Language Models in Ophthalmology: Clinical Applications and Challenges
title_sort generative artificial intelligence through chatgpt and other large language models in ophthalmology: clinical applications and challenges
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10598525/
https://www.ncbi.nlm.nih.gov/pubmed/37885755
http://dx.doi.org/10.1016/j.xops.2023.100394
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