ArticleJournal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology2025
Evaluating AI proficiency in nuclear cardiology: Large language models take on the board preparation exam.
Article in Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- Performance of large language models on undergraduate endodontic multiple-choice questions.BMC oral health · 2026Article
- Evaluation of the performance of ChatGPT-4 and ChatGPT-4o as a learning tool in endodontics.International endodontic journal · 2026Article
- Three large language models demonstrate competitive performance in Traditional Chinese Medicine national medical licensing examinations over two years.Scientific reports · 2026Article
- Comparative performance of large language models in answering cornea and cataract surgery questions for resident training.BMC ophthalmology · 2026Article
- From algorithms to operating room: can large language models master China's attending anesthesiology exam? A cross-sectional evaluation.International journal of surgery (London, England) · 2026Article
- Evaluating the performance of GPT-3.5, GPT-4, and GPT-4o in the Chinese National Medical Licensing Examination.Scientific reports · 2025Article
- Extracting Pulmonary Embolism Diagnoses From Radiology Impressions Using GPT-4o: Large Language Model Evaluation Study.JMIR medical informatics · 2025Article
- Accuracy of ChatGPT in answering cardiology board-style questions.Journal of educational evaluation for health professions · 2025Article
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Abstract
backgroundPrevious studies evaluated the ability of large language models (LLMs) in medical disciplines; however, few have focused on image analysis, and none specifically on cardiovascular imaging or nuclear cardiology. This study assesses four LLMs-GPT-4, GPT-4 Turbo, GPT-4omni (GPT-4o) (Open AI), and Gemini (Google Inc.)-in responding to questions from the 2023 American Society of Nuclear Cardiology Board Preparation Exam, reflecting the scope of the Certification Board of Nuclear Cardiology (CBNC) examination.
methodsWe used 168 questions: 141 text-only and 27 image-based, categorized into four sections mirroring the CBNC exam. Each LLM was presented with the same standardized prompt and applied to each section 30 times to account for stochasticity. Performance over six weeks was assessed for all models except GPT-4o. McNemar's test compared correct response proportions.
resultsGPT-4, Gemini, GPT-4 Turbo, and GPT-4o correctly answered median percentages of 56.8% (95% confidence interval 55.4% - 58.0%), 40.5% (39.9% - 42.9%), 60.7% (59.5% - 61.3%), and 63.1% (62.5%-64.3%) of questions, respectively. GPT-4o significantly outperformed other models (P = .007 vs GPT-4 Turbo, P < .001 vs GPT-4 and Gemini). GPT-4o excelled on text-only questions compared to GPT-4, Gemini, and GPT-4 Turbo (P < .001, P < .001, and P = .001), while Gemini performed worse on image-based questions (P < .001 for all).
conclusionGPT-4o demonstrated superior performance among the four LLMs, achieving scores likely within or just outside the range required to pass a test akin to the CBNC examination. Although improvements in medical image interpretation are needed, GPT-4o shows potential to support physicians in answering text-based clinical questions.
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