Cargando…
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,”...
Autores principales: | , , , , , , , , , , |
---|---|
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 |
_version_ | 1785125572809064448 |
---|---|
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. |
format | Online Article Text |
id | pubmed-10598525 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT tantingfang generativeartificialintelligencethroughchatgptandotherlargelanguagemodelsinophthalmologyclinicalapplicationsandchallenges AT thirunavukarasuarunjames generativeartificialintelligencethroughchatgptandotherlargelanguagemodelsinophthalmologyclinicalapplicationsandchallenges AT campbelljpeter generativeartificialintelligencethroughchatgptandotherlargelanguagemodelsinophthalmologyclinicalapplicationsandchallenges AT keanepearsea generativeartificialintelligencethroughchatgptandotherlargelanguagemodelsinophthalmologyclinicalapplicationsandchallenges AT pasqualelouisr generativeartificialintelligencethroughchatgptandotherlargelanguagemodelsinophthalmologyclinicalapplicationsandchallenges AT abramoffmichaeld generativeartificialintelligencethroughchatgptandotherlargelanguagemodelsinophthalmologyclinicalapplicationsandchallenges AT kalpathycramerjayashree generativeartificialintelligencethroughchatgptandotherlargelanguagemodelsinophthalmologyclinicalapplicationsandchallenges AT lumflora generativeartificialintelligencethroughchatgptandotherlargelanguagemodelsinophthalmologyclinicalapplicationsandchallenges AT kimjudye generativeartificialintelligencethroughchatgptandotherlargelanguagemodelsinophthalmologyclinicalapplicationsandchallenges AT baxtersallyl generativeartificialintelligencethroughchatgptandotherlargelanguagemodelsinophthalmologyclinicalapplicationsandchallenges AT tingdanielshuwei generativeartificialintelligencethroughchatgptandotherlargelanguagemodelsinophthalmologyclinicalapplicationsandchallenges |