ArticleBMC emergency medicine2025
Can AI match emergency physicians in managing common emergency cases? A comparative performance evaluation.
Article in BMC emergency medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Comparative Expert Evaluation of Multimodal Large Language Models for Pediatric Rash Diagnosis: Clinical Utility, Safety, Information Quality, and Readability.Children (Basel, Switzerland) · 2026Article
- Acceptance of Technological Innovations in Emergency Departments: An Empirical Study Based on an Extended TAM.Healthcare (Basel, Switzerland) · 2026Article
- Can ChatGPT compete with emergency medicine specialists? A two-stage assessment of ECG interpretation supported by clinical information.BMC emergency medicine · 2026Article
- Evaluation of ChatGPT-5 responses in obstetric and gynecological emergencies: concordance, readability, and clinical reliability.BMC emergency medicine · 2025Article
- Comparing Artificial intelligence to physicians' competences in the domain of clinical reasoning: A systematic review and meta-analysis.Journal of medical education and curricular developmentReview
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1 author.
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Abstract
backgroundLarge language models (LLMs) such as ChatGPT are increasingly explored for clinical decision support. However, their performance in high-stakes emergency scenarios remains underexamined. This study aimed to evaluate ChatGPT's diagnostic and therapeutic accuracy compared to a board-certified emergency physician across diverse emergency cases.
methodsThis comparative study was conducted using 15 standardized emergency scenarios sourced from validated academic platforms (Geeky Medics, Life in the Fast Lane, Emergency Medicine Cases). ChatGPT (GPT-4) and a physician independently evaluated each case based on five predefined parameters: diagnosis, investigations, initial treatment, clinical safety, and decision-making complexity. Cases were scored out of 5. Concordance was categorized as high (5/5), moderate (4/5), or low (≤ 3/5). Wilson confidence intervals (95%) were calculated for each concordance category.
resultsChatGPT achieved high concordance (5/5) in 8 cases (53.3%, 95% CI: 27.6-77.0%), moderate concordance (4/5) in 4 cases (26.7%, CI: 10.3-55.4%), and low concordance (≤ 3/5) in 3 cases (20.0%, CI: 6.0-45.6%). Performance was strongest in structured, protocol-based conditions such as STEMI, DKA, and asthma. Lower performance was observed in complex scenarios like stroke, trauma with shock, and mixed acid-base disturbances.
conclusionChatGPT showed strong alignment with emergency physician decisions in structured scenarios but lacked reliability in complex cases. While AI may enhance decision-making and education, it cannot replace the clinical reasoning of human physicians. Its role is best framed as a supportive tool rather than a substitute.
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