Journal article
Generative Artificial Intelligence through ChatGPT and Other Large Language Models in Ophthalmology: Clinical Applications and Challenges
Ophthalmology science (Online), Vol.3(4), 100394
12/2023
DOI: 10.1016/j.xops.2023.100394
PMCID: PMC10598525
PMID: 37885755
Abstract
The rapid progress of large language models (LLMs) driving generative artificial intelligence applications heralds the potential of opportunities in healthcare. 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.
Details
- Title: Subtitle
- Generative Artificial Intelligence through ChatGPT and Other Large Language Models in Ophthalmology: Clinical Applications and Challenges
- Creators
- Ting Fang Tan - Singapore National Eye CenterArun James Thirunavukarasu - University of CambridgeJ Peter Campbell - Oregon Health & Science UniversityPearse A. Keane - Moorfields Eye HospitalLouis R. Pasquale - Icahn School of Medicine at Mount SinaiMichael D. Abramoff - University of IowaJayashree Kalpathy-Cramer - Athinoula A. Martinos Center for Biomedical ImagingFlora Lum - American Academy of OphthalmologyJudy E. Kim - Medical College of WisconsinSally L. Baxter - University of California San DiegoDaniel Shu Wei Ting - Singapore National Eye Center
- Resource Type
- Journal article
- Publication Details
- Ophthalmology science (Online), Vol.3(4), 100394
- DOI
- 10.1016/j.xops.2023.100394
- PMID
- 37885755
- PMCID
- PMC10598525
- NLM abbreviation
- Ophthalmol Sci
- ISSN
- 2666-9145
- eISSN
- 2666-9145
- Publisher
- Elsevier Inc
- Language
- English
- Electronic publication date
- 09/2023
- Date published
- 12/2023
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Fraternal Order of Eagles Diabetes Research Center; Ophthalmology and Visual Sciences
- Record Identifier
- 9984465458302771
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