Investigative Ophthalmology & Visual Science Cover Image for Volume 65, Issue 7
June 2024
Volume 65, Issue 7
Open Access
ARVO Annual Meeting Abstract  |   June 2024
From Accuracy, Comprehensiveness, to Self-Awareness: A Deep Dive into Large Language Models' Performances in Ophthalmology"
Author Affiliations & Notes
  • Yih Chung Tham
    Ophthalmology, National University of Singapore Yong Loo Lin School of Medicine, Singapore, Singapore
    Ocular Epidemiology, Singapore Eye Research Institute, Singapore, Singapore
  • Footnotes
    Commercial Relationships   Yih Chung Tham None
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science June 2024, Vol.65, 3877. doi:
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      Yih Chung Tham; From Accuracy, Comprehensiveness, to Self-Awareness: A Deep Dive into Large Language Models' Performances in Ophthalmology". Invest. Ophthalmol. Vis. Sci. 2024;65(7):3877.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Presentation Description : In this talk, I will explore the growing role and evaluation metrics of AI-driven language models in ophthalmic healthcare. Beginning with an assessment of their accuracy in addressing patient queries and medical text mining, the discussion will then extend to the comprehensiveness of these models in understanding the domain-specific knowledge of ophthalmology. Special attention will be given to the self-awareness of these algorithms, focusing on their limitations and ethical considerations. The talk aims to present a balanced view by showcasing real-world applications while also underlining existing constraints and areas for future research.

This abstract was presented at the 2024 ARVO Annual Meeting, held in Seattle, WA, May 5-9, 2024.

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