Citation

Gan, Rick Kye and Ogbodo, Jude Chukwuebuka and Wee, Yong Zheng and Gan, Ann Zee and González, Pedro Arcos (2024) Performance of Google bard and ChatGPT in mass casualty incidents triage. The American Journal of Emergency Medicine, 75. pp. 72-78. ISSN 0735-6757

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Abstract

Aim: The objective of our research is to evaluate and compare the performance of ChatGPT, Google Bard, and medical students in performing START triage during mass casualty situations. Method: We conducted a cross-sectional analysis to compare ChatGPT, Google Bard, and medical students in mass casualty incident (MCI) triage using the Simple Triage And Rapid Treatment (START) method. A validated questionnaire with 15 diverse MCI scenarios was used to assess triage accuracy and content analysis in four categories: “Walking wounded,” “Respiration,” “Perfusion,” and “Mental Status.” Statistical analysis compared the results. Result: Google Bard demonstrated a notably higher accuracy of 60%, while ChatGPT achieved an accuracy of 26.67% (p = 0.002). Comparatively, medical students performed at an accuracy rate of 64.3% in a previous study. However, there was no significant difference observed between Google Bard and medical students (p = 0.211). Qualitative content analysis of ‘walking-wounded’, ‘respiration’, ‘perfusion’, and ‘mental status’ indicated that Google Bard outperformed ChatGPT. Conclusion: Google Bard was found to be superior to ChatGPT in correctly performing mass casualty incident triage. Google Bard achieved an accuracy of 60%, while chatGPT only achieved an accuracy of 26.67%. This difference was statistically significant (p = 0.002).

Item Type:	Article
Uncontrolled Keywords:	Artificial intelligence, disaster medicine
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics R Medicine > RA Public aspects of medicine > RA421-790.95 Public health. Hygiene. Preventive medicine
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	04 Jan 2024 01:41
Last Modified:	04 Jan 2024 01:41
URII:	http://shdl.mmu.edu.my/id/eprint/12021

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