JMIR AI · 2026
AI in Disaster Medicine: Scoping Review of Methods, Validation, and System Integration.
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Peralta Ruben, Msheik Ali, Al Mokdad Zeinab, Yigit Yavuz, Al-Thani Hassan, Al Rumaihi Ghaya, Al-Sulaiti Ghanem, Cameron Peter
Аннотация
BACKGROUND: AI is increasingly proposed as a tool to enhance disaster medicine through improved situational awareness, decision support, and resource coordination. However, the extent to which current research has progressed beyond methodological development toward integrated, operationally validated systems remains unclear.
OBJECTIVE: This review aimed to systematically map the scope, methods, validation strategies, and system integration of AI applications in disaster medicine and emergency health care systems.
METHODS: This scoping review was conducted in accordance with PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. The PubMed (MEDLINE), Scopus, IEEE Xplore, and Google Scholar databases were searched from inception to January 31, 2026. Studies describing AI applications in disaster medicine, emergency response, mass casualty care, or public health emergencies were eligible. Data were charted across the emergency domain, scenario type, AI function, study design, and validation level.
RESULTS: A total of 168 studies were included. Research activity was concentrated in the disaster response and rescue, and public health and pandemics domains, which together accounted for 64 (38.1%) studies. Most studies involved algorithm or model development (43/168, 25.6%) or system or tool development (33/168, 19.6%), whereas applied and observational studies were less common (15/168, 8.9%). Validation was predominantly internal or simulation-based; external validation was reported in 13 (7.7%) studies, and prospective real-world validation was reported in 2 (1.2%) studies. Human-centered, smart city, and mental health domains were consistently underrepresented.
CONCLUSIONS: AI research in disaster medicine is expanding rapidly but remains fragmented and is at an early stage of translational maturity. Future progress will depend on system-level integration, rigorous real-world validation, and alignment with operational emergency workflows.
OBJECTIVE: This review aimed to systematically map the scope, methods, validation strategies, and system integration of AI applications in disaster medicine and emergency health care systems.
METHODS: This scoping review was conducted in accordance with PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. The PubMed (MEDLINE), Scopus, IEEE Xplore, and Google Scholar databases were searched from inception to January 31, 2026. Studies describing AI applications in disaster medicine, emergency response, mass casualty care, or public health emergencies were eligible. Data were charted across the emergency domain, scenario type, AI function, study design, and validation level.
RESULTS: A total of 168 studies were included. Research activity was concentrated in the disaster response and rescue, and public health and pandemics domains, which together accounted for 64 (38.1%) studies. Most studies involved algorithm or model development (43/168, 25.6%) or system or tool development (33/168, 19.6%), whereas applied and observational studies were less common (15/168, 8.9%). Validation was predominantly internal or simulation-based; external validation was reported in 13 (7.7%) studies, and prospective real-world validation was reported in 2 (1.2%) studies. Human-centered, smart city, and mental health domains were consistently underrepresented.
CONCLUSIONS: AI research in disaster medicine is expanding rapidly but remains fragmented and is at an early stage of translational maturity. Future progress will depend on system-level integration, rigorous real-world validation, and alignment with operational emergency workflows.