ArticleCureus2024
Performance of Google's Artificial Intelligence Chatbot "Bard" (Now "Gemini") on Ophthalmology Board Exam Practice Questions.
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.
What it found
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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.
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.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evaluating Large Language Models in Ophthalmology: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Comparison of the ChatGPT and deepseek models in responding to multiple choice questions related to rehabilitation of completely edentulous patients with complete dentures.BMC oral health · 2026Article
- A comparative analysis of the performance of large Language models in the dentistry specialty examination.Scientific reports · 2026Article
- Performance of the Artificial Intelligence large language models ChatGPT 3.5, Gemini (Google Bard), ChatGPT 4.0, and Gemini 2.5 flash in surgical subspecialty questions of Brazilian medical residency exams.Einstein (Sao Paulo, Brazil) · 2026Article
- Comparative performance of GPT-4, GPT-o3, GPT-5, Gemini-3-Flash, and DeepSeek-R1 in ophthalmology question answering.Frontiers in cell and developmental biology · 2026Article
- Applications, Challenges, and Prospects of Generative Artificial Intelligence Empowering Medical Education: Scoping Review.JMIR medical education · 2025Article
- Large language models in ophthalmology: a scoping review on their utility for clinicians, researchers, patients, and educators.Eye (London, England) · 2025Article
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- A bi-linguistic comparative analysis of ChatGPT-4, Gemini, and Claude performance on Polish medical-dental final examinations.Scientific reports · 2025Article
- Performance of ChatGPT-4 Omni and Gemini 1.5 Pro on Ophthalmology-Related Questions in the Turkish Medical Specialty Exam.Turkish journal of ophthalmology · 2025Article
- 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 · 2025Article
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- Opportunities and Challenges of Chatbots in Ophthalmology: A Narrative Review.Journal of personalized medicine · 2024Review
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- Evaluating AI Proficiency in Nuclear Cardiology: Large Language Models take on the Board Preparation Exam.medRxiv : the preprint server for health sciences · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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.