Observational studyJournal of clinical nursing2026
Evaluating Large Language Model-Assisted Emergency Triage: A Comparison of Acuity Assessments by GPT-4 and Medical Experts.
Observational study in Journal of clinical nursing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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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
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic accuracy of large language models for emergency department triage: a systematic review and meta-analysis.BMC emergency medicine · 2026Pooled it
- Evaluating Large Language Model-Assisted Emergency Triage: A Comparison of Acuity Assessments by GPT-4 and Medical Experts.Journal of clinical nursing · 2026Observational
- The Promise and Peril of Generative AI in Nursing: Moving Beyond Hype Toward Responsible Innovation.Journal of clinical nursing · 2026Article
- From assistant to collaborator: A systematic review of the evolution of artificial intelligence in end-stage renal disease care and management.PLOS digital health · 2026Article
- AI at the Sella Turcica: Multi-Model Large Language Model Evaluation in Pituitary Adenomas.Brain & spine · 2026Article
- TC3-VLM: A Vision-Language Model for Tactical Combat Casualty Care.IEEE access : practical innovations, open solutions · 2026Article
- Large language models in emergency medicine education: opportunities, challenges, and implementation pathways.Frontiers in public health · 2026Review
- Agreement of Feline Grimace Scale scores between chatbots and an expert rater.Scientific reports · 2025Article
- Safety and accuracy of AI in triaging patients in the emergency department.International journal of emergency medicine · 2025Article
- Diagnosis and Triage Performance of Contemporary Large Language Models on Short Clinical Vignettes.Journal of medical systems · 2025Article
- Evaluating large language models on hospital health data for automated emergency triage.International journal of computer assisted radiology and surgery · 2025Article
- Mapping artificial intelligence models in emergency medicine: A scoping review on artificial intelligence performance in emergency care and education.Turkish journal of emergency medicineReview
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimTo evaluate the accuracy of the Emergency Severity Index (ESI) assignments by GPT-4, a large language model (LLM), compared to senior emergency department (ED) nurses and physicians.
methodAn observational study of 100 consecutive adult ED patients was conducted. ESI scores assigned by GPT-4, triage nurses, and by a senior clinician. Both model and human experts were provided the same patient data.
resultsGPT-4 assigned a lower median ESI score (2.0) compared to human evaluators (median 3.0; p < 0.001), suggesting a potential overestimation of patient severity by the LLM. The results showed differences in the triage assessment approaches between GPT-4 and the human evaluators, including variations in how patient age and vital signs were considered in the ESI assignments.
conclusionWhile GPT-4 offers a novel methodology for patient triage, its propensity to overestimate patient severity highlights the necessity for further development and calibration of LLM tools in clinical environments. The findings underscore the potential and limitations of LLM in clinical decision-making, advocating for cautious integration of LLMs in healthcare settings. REPORTING
methodThis study adhered to relevant EQUATOR guidelines for reporting observational studies.
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Registered trials
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