ArticleBMC emergency medicine2025
AI-assisted decision-making in mild traumatic brain injury.
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, 1 of them a synthesis that pooled it.
What it found
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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
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of ethical considerations of large language models in healthcare and medicine.Frontiers in digital health · 2025Pooled it
- Large Language Model Decision Support for Cranial CT in Pediatric Head Trauma.Diagnostics (Basel, Switzerland) · 2026Article
- Comparing the text-based diagnostic reasoning performance of emergency medicine physicians and large language models in both definitive and differential diagnoses using standardized clinical vignettes: a preliminary study.International journal of emergency medicine · 2026Article
- Evaluation of ChatGPT-5 for CT Imaging in Canadian CT Head Rule-Positive Mild Traumatic Brain Injury: A Pilot Study.Biomedicines · 2026Article
- Pediatric head injury in the emergency department: balancing radiation risk, missed diagnoses, and decision-rule evolution.International journal of emergency medicine · 2026Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study evaluates the potential use of ChatGPT in aiding clinical decision-making for patients with mild traumatic brain injury (TBI) by assessing the quality of responses it generates for clinical care.
methodsSeventeen mild TBI case scenarios were selected from PubMed Central, and each case was analyzed by GPT-4 (March 21, 2024, version) between April 11 and April 20, 2024. Responses were evaluated by four emergency medicine specialists, who rated the ease of understanding, scientific adequacy, and satisfaction with each response using a 7-point Likert scale. Evaluators were also asked to identify critical errors, defined as mistakes in clinical care or interpretation that could lead to morbidity or mortality. The readability of GPT-4's responses was also assessed using the Flesch Reading Ease and Flesch-Kincaid Grade Level tools.
resultsThere was no significant difference in the ease of understanding between responses with and without critical errors (p = 0.133). However, responses with critical errors significantly reduced satisfaction and scientific adequacy (p < 0.001). GPT-4 responses were significantly more difficult to read than the case descriptions (p < 0.001).
conclusionGPT-4 demonstrates potential utility in clinical decision-making for mild TBI management, offering scientifically appropriate and comprehensible responses. However, critical errors and readability issues limit its immediate implementation in emergency settings without oversight by experienced medical professionals.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.