ArticleThe American journal of emergency medicine2025
Automated computation of the HEART score with the GPT-4 large language model.
Article in The American journal of emergency medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Improving End-of-Life Screening in the Emergency Department With Collaborative Artificial Intelligence.Annals of emergency medicine · 2026Article
- Performance of GPT-based large language models in hepatocellular carcinoma stratification: liver function assessment, BCLC staging, and treatment recommendations.Scientific reports · 2026Article
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Authors and funding
13 authors.
Funding
Abstract
backgroundAutomated computation of the HEART score has the potential to facilitate clinical decision support and safety interventions. The goal of this study was to assess the performance of the GPT-4 large language model (LLM) in computation of the HEART score and prediction of 60-day major adverse cardiac events (MACE).
methodsIn this retrospective cohort study from February 2022 to September 2023, patients admitted to a chest pain observation unit were identified. HEART scores were calculated by a physician assistant or nurse practitioner (APP) as part of routine care. Separately, the LLM calculated a HEART score utilizing an iteratively developed prompt from deidentified chart documentation. Any cases of disagreement with the APP score were adjudicated by an emergency physician blinded to clinical outcomes. Agreement on HEART score was assessed, and 60-day MACE was obtained via linkage to an institutional registry.
resultsOf the 601 participants, 50 were utilized for prompt development. Among the remaining 551 participants, agreement by Cohen's weighted kappa between the LLM and adjudicators was 0.67 which was similar to the agreement of 0.66 between the APP and adjudicators. The LLM predicted a higher average HEART score (mean 5.06) compared to the adjudicators (mean 4.69) or APP (mean 4.23). No significant difference was seen in diagnostic performance for 60-day MACE by DeLong pairwise comparison (all p > .05).
conclusionsAutomated risk score computation with language models has the potential to power interventions such as clinical decision support but has systematic differences from physician judgment. Prospective investigation is needed.
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