Utility of artificial intelligence-based large language models in ophthalmic care


Purpose: With the introduction of ChatGPT, artificial intelligence (AI)-based large language models (LLMs) are rapidly becoming popular within the scientific community. They use natural language processing to generate human-like responses to queries. However, the application of LLMs and comparison of the abilities among different LLMs with their human counterparts in ophthalmic care remain under-reported. Recent Findings: Hitherto, studies in eye care have demonstrated the utility of ChatGPT in generating patient information, clinical diagnosis and passing ophthalmology question-based examinations, among others. LLMs' performance (median accuracy, %) is influenced by factors such as the iteration, prompts utilised and the domain. Human expert (86%) demonstrated the highest proficiency in disease diagnosis, while ChatGPT-4 outperformed others in ophthalmology examinations (75.9%), symptom triaging (98%) and providing information and answering questions (84.6%). LLMs exhibited superior performance in general ophthalmology but reduced accuracy in ophthalmic subspecialties. Although AI-based LLMs like ChatGPT are deemed more efficient than their human counterparts, these AIs are constrained by their nonspecific and outdated training, no access to current knowledge, generation of plausible-sounding ‘fake’ responses or hallucinations, inability to process images, lack of critical literature analysis and ethical and copyright issues. A comprehensive evaluation of recently published studies is crucial to deepen understanding of LLMs and the potential of these AI-based LLMs. Summary: Ophthalmic care professionals should undertake a conservative approach when using AI, as human judgement remains essential for clinical decision-making and monitoring the accuracy of information. This review identified the ophthalmic applications and potential usages which need further exploration. With the advancement of LLMs, setting standards for benchmarking and promoting best practices is crucial. Potential clinical deployment requires the evaluation of these LLMs to move away from artificial settings, delve into clinical trials and determine their usefulness in the real world.

Publication DOI: https://doi.org/10.1111/opo.13284
Divisions: College of Health & Life Sciences > School of Optometry > Optometry
College of Health & Life Sciences
College of Health & Life Sciences > School of Optometry > Optometry & Vision Science Research Group (OVSRG)
College of Health & Life Sciences > School of Optometry > Vision, Hearing and Language
Additional Information: Copyright © 2024, The Authors. Ophthalmic and Physiological Optics published by John Wiley & Sons Ltd on behalf of College of Optometrists. This is an open access article under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Uncontrolled Keywords: artificial intelligence,chatbot,large language model,opthalmic care,opthalmology,optometry
Publication ISSN: 1475-1313
Last Modified: 11 Jul 2024 07:26
Date Deposited: 26 Feb 2024 18:00
Full Text Link:
Related URLs: https://onlinel ... .1111/opo.13284 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Review article
Published Date: 2024-05
Published Online Date: 2024-02-25
Accepted Date: 2024-01-25
Authors: Biswas, Sayantan (ORCID Profile 0000-0001-6011-0365)
Davies, Leon N. (ORCID Profile 0000-0002-1554-0566)
Sheppard, Amy L. (ORCID Profile 0000-0003-0035-8267)
Logan, Nicola S. (ORCID Profile 0000-0002-0538-9516)
Wolffsohn, James S. (ORCID Profile 0000-0003-4673-8927)



Version: Published Version

License: Creative Commons Attribution

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