ArticleJournal of medical Internet research2024
ChatGPT's Performance in Cardiac Arrest and Bradycardia Simulations Using the American Heart Association's Advanced Cardiovascular Life Support Guidelines: Exploratory Study.
Article in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Feasibility and challenges of large language model (LLM)-generated neonatal resuscitation simulations: a multicenter exploratory study.Scandinavian journal of trauma, resuscitation and emergency medicine · 2026Article
- Large Language Models in Patient Health Communication for Atherosclerotic Cardiovascular Disease: Pilot Cross-Sectional Comparative Analysis.JMIR medical informatics · 2026Article
- Artificial intelligence based resuscitation simulation: a pilot study of a novel approach to team leadership training.BMC medical education · 2026Article
- Large language models in emergency and critical care medicine: a comprehensive review of applications, challenges, and future directions.Burns & trauma · 2026Review
- Large language models in emergency medicine education: opportunities, challenges, and implementation pathways.Frontiers in public health · 2026Review
- Large Language Models in Critical Care Medicine: Scoping Review.JMIR medical informatics · 2025Article
- Applications of large language models in cardiovascular disease: a systematic review.European heart journal. Digital health · 2025Review
- Qwen-2.5 Outperforms Other Large Language Models in the Chinese National Nursing Licensing Examination: Retrospective Cross-Sectional Comparative Study.JMIR medical informatics · 2025Article
- Article
- AI in Dental Radiology-Improving the Efficiency of Reporting With ChatGPT: Comparative Study.Journal of medical Internet research · 2024Article
- Harnessing the Power of ChatGPT in Cardiovascular Medicine: Innovations, Challenges, and Future Directions.Journal of clinical medicine · 2024Review
- Article
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6 authors.
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Abstract
backgroundChatGPT is the most advanced large language model to date, with prior iterations having passed medical licensing examinations, providing clinical decision support, and improved diagnostics. Although limited, past studies of ChatGPT's performance found that artificial intelligence could pass the American Heart Association's advanced cardiovascular life support (ACLS) examinations with modifications. ChatGPT's accuracy has not been studied in more complex clinical scenarios. As heart disease and cardiac arrest remain leading causes of morbidity and mortality in the United States, finding technologies that help increase adherence to ACLS algorithms, which improves survival outcomes, is critical.
objectiveThis study aims to examine the accuracy of ChatGPT in following ACLS guidelines for bradycardia and cardiac arrest.
methodsWe evaluated the accuracy of ChatGPT's responses to 2 simulations based on the 2020 American Heart Association ACLS guidelines with 3 primary outcomes of interest: the mean individual step accuracy, the accuracy score per simulation attempt, and the accuracy score for each algorithm. For each simulation step, ChatGPT was scored for correctness (1 point) or incorrectness (0 points). Each simulation was conducted 20 times.
resultsChatGPT's median accuracy for each step was 85% (IQR 40%-100%) for cardiac arrest and 30% (IQR 13%-81%) for bradycardia. ChatGPT's median accuracy over 20 simulation attempts for cardiac arrest was 69% (IQR 67%-74%) and for bradycardia was 42% (IQR 33%-50%). We found that ChatGPT's outputs varied despite consistent input, the same actions were persistently missed, repetitive overemphasis hindered guidance, and erroneous medication information was presented.
conclusionsThis study highlights the need for consistent and reliable guidance to prevent potential medical errors and optimize the application of ChatGPT to enhance its reliability and effectiveness in clinical practice.
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