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
Performance of “Bard”, Google’s Artificial Intelligence Chatbot, on Ophthalmology Board Exam Practice Questions
Author Affiliations & Notes
  • Monica Botross
    Burnett School of Medicine, Texas Christian University, Fort Worth, Texas, United States
  • Seyed Omid Mohammadi
    Burnett School of Medicine, Texas Christian University, Fort Worth, Texas, United States
  • Kendall Montgomery
    Burnett School of Medicine, Texas Christian University, Fort Worth, Texas, United States
  • Footnotes
    Commercial Relationships   Monica Botross None; Seyed Omid Mohammadi None; Kendall Montgomery None
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science June 2024, Vol.65, 358. doi:
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      Monica Botross, Seyed Omid Mohammadi, Kendall Montgomery; Performance of “Bard”, Google’s Artificial Intelligence Chatbot, on Ophthalmology Board Exam Practice Questions. Invest. Ophthalmol. Vis. Sci. 2024;65(7):358.

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

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Abstract

Purpose : To assess the performance of “Bard”, one of ChatGPT’s competitors, in answering practice questions for the ophthalmology board certification exam.

Methods : In December 2023, 250 multiple-choice questions from the “BoardVitals” ophthalmology exam question bank were randomly selected and inputted into Bard to assess the artificial intelligence chatbot’s ability to comprehend, process, and answer complex scientific and clinical ophthalmic questions. A random mix of text-only and image-and-text questions were selected from 10 subsections. Each subsection included 25 questions. The percentage of correct responses was calculated per section and an overall assessment score was determined.

Results : On average, Bard answered 62.4% of questions correctly. The worst performance was 24% correct on the topic of “Retina and Vitreous”, and the best performance was on “Oculoplastics” with a score of 84% correct. While the majority of questions were inputted with minimal difficulty, not all questions were able to be processed by Bard, particularly questions that included human images and multiple visual files. Some vignette-style questions were also not understood by Bard and were therefore omitted. Future investigations will focus on including more questions per subsection to increase available data points.

Conclusions : While Bard answered 62.4% of questions correctly and is capable of analyzing vast amounts of medical data, it ultimately lacks the holistic understanding and clinical experience of an ophthalmologist. Physicians’ judgment in making a medical diagnosis remains irreplaceable and artificial intelligence should be employed as a valuable tool for supplementing, rather than replacing, medical diagnosis.

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

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