ChatGPT's performance before and after teaching in mass casualty incident triage

Abstract

Since its initial launching, ChatGPT has gained significant attention from the media, with many claiming that ChatGPT's arrival is a transformative milestone in the advancement of the AI revolution. Our aim was to assess the performance of ChatGPT before and after teaching the triage of mass casualty incidents by utilizing a validated questionnaire specifically designed for such scenarios. In addition, we compared the triage performance between ChatGPT and medical students. Our cross-sectional study employed a mixed-methods analysis to assess the performance of ChatGPT in mass casualty incident triage, pre- and post-teaching of Simple Triage And Rapid Treatment (START) triage. After teaching the START triage algorithm, ChatGPT scored an overall triage accuracy of 80%, with only 20% of cases being over-triaged. The mean accuracy of medical students on the same questionnaire yielded 64.3%. Qualitative analysis on pre-determined themes on 'walking-wounded', 'respiration', 'perfusion', and 'mental status' on ChatGPT showed similar performance in pre- and post-teaching of START triage. Additional themes on 'disclaimer', 'prediction', 'management plan', and 'assumption' were identified during the thematic analysis. ChatGPT exhibited promising results in effectively responding to mass casualty incident questionnaires. Nevertheless, additional research is necessary to ensure its safety and efficacy before clinical implementation.

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