ArticleCureus2025
Comparative Analysis of Large Language Models in First-Aid Scenario Recognition and Management: An In Silico Evaluation of ChatGPT and Claude.
Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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5 authors.
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Abstract
introductionLarge language models (LLMs) deliver real-time, conversational guidance, yet their reliability for time-critical first aid remains unclear. MATERIALS AND
methodsFive standardized vignettes (drowning, animal bite, opioid overdose, lightning strike, and frostbite) were presented three times each to GPT-4o (OpenAI, San Francisco, CA, USA) and Claude 3.5 Sonnet (Anthropic, San Francisco, CA, USA). Outputs were scored (0 = incorrect/unsafe, 1 = incomplete, 2 = entirely correct) across six domains: diagnostic accuracy, first-aid advice, triage accuracy, comprehensiveness, safety, and consistency. Scores were averaged within and across vignettes.
resultsBoth LLMs achieved perfect diagnostic (2.0) and triage (2.0) scores. Claude 3.5 outperformed GPT-4o in first-aid accuracy (2.0 vs 1.5), comprehensiveness (1.5 vs 1.3), and consistency (2.0 vs 1.6). Safety ratings were comparable (1.9-2.0). Key GPT-4 omissions included naloxone administration for opioid overdose and immediate sheltering guidance after a lightning strike.
conclusionsClaude 3.5 provided more complete and stable first-aid guidance than GPT-4, although both models reliably identified emergencies and advised on the appropriate escalation of care. Wider implementation warrants larger vignette sets, real-user simulations, and continuous monitoring for guideline concordance.
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