Evidence map›Paper›PMID 38690460›Full record

ArticleCureus2024

Performance of Google's Artificial Intelligence Chatbot "Bard" (Now "Gemini") on Ophthalmology Board Exam Practice Questions.

Monica Botross, Seyed Omid Mohammadi, Kendall Montgomery, Courtney Crawford

Abstract read
In one paragraph

Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  11. Evaluating AI proficiency in nuclear cardiology: Large language models take on the board preparation exam.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Monica BotrossOphthalmology, Burnett School of Medicine at Texas Christian University, Fort Worth, USA.
Seyed Omid MohammadiOphthalmology, Burnett School of Medicine at Texas Christian University, Fort Worth, USA.
Kendall MontgomeryOphthalmology, Burnett School of Medicine at Texas Christian University, Fort Worth, USA.
Courtney CrawfordOphthalmology, Burnett School of Medicine at Texas Christian University, Fort Worth, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo assess the performance of "Bard," one of ChatGPT's competitors, in answering practice questions for the ophthalmology board certification exam.

methodsIn December 2023, 250 multiple-choice questions from the "BoardVitals" ophthalmology exam question bank were randomly selected and entered 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.

resultsOn average, Bard answered 62.4% (156/250) of questions correctly. The worst performance was 24% (6/25) on the topic of "Retina and Vitreous," and the best performance was on "Oculoplastics," with a score of 84% (21/25). While the majority of questions were entered with minimal difficulty, not all questions could be processed by Bard. This was particularly an issue for 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 having more questions per subsection to increase available data points.

conclusionsWhile Bard answered the majority of questions correctly and is capable of analyzing vast amounts of medical data, it ultimately lacks the holistic understanding and experience-informed knowledge of an ophthalmologist. An ophthalmologist's ability to synthesize diverse pieces of information and draw from clinical experience to answer complex standardized board questions is at present irreplaceable, and artificial intelligence, in its current form, can be employed as a valuable tool for supplementing clinicians' study methods.

Indexed as

american board of ophthalmologyartificial intelligencebardchat gptgeminillmmedical educationokapophthalmologywqe

Identifiers

PMID38690460
PMCPMC11060832

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.