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AI in Disaster Medicine: Scoping Review of Methods, Validation, and System Integration
Ruben Peralta1,2, Ali Msheik3, Zeinab Al Mokdad4
1Trauma Surgery, Hamad Medical Corporation, Doha, Baladīyat ad Dawḩah, Qatar.
JMIR AI
|August 4, 2026
Summary
Artificial intelligence (AI) in disaster medicine is rapidly advancing but lacks real-world validation and integration. Future progress requires system integration and operational alignment for effective emergency response.
Area of Science:
- Disaster medicine
- Emergency health care systems
- Artificial Intelligence (AI) applications
Background:
- AI is proposed to enhance disaster medicine through improved situational awareness, decision support, and resource coordination.
- Current research progress beyond methodological development toward integrated, operationally validated systems remains unclear.
Purpose of the Study:
- To systematically map the scope, methods, validation strategies, and system integration of AI applications in disaster medicine and emergency health care systems.
Main Methods:
- Scoping review conducted following PRISMA-ScR guidelines.
- Searched PubMed, Scopus, IEEE Xplore, and Google Scholar databases from inception to January 31, 2026.
- Included studies on AI in disaster medicine, emergency response, mass casualty care, or public health emergencies; data charted across emergency domain, scenario type, AI function, study design, and validation level.
Main Results:
- 168 studies included, with research concentrated in disaster response/rescue and public health/pandemics (64 studies).
- Most studies focused on algorithm/model development (25.6%) or system/tool development (19.6%), with fewer applied/observational studies (8.9%).
- Validation was predominantly internal/simulation-based; external validation (7.7%) and prospective real-world validation (1.2%) were rare. Human-centered, smart city, and mental health domains were underrepresented.
Conclusions:
- AI research in disaster medicine is expanding but fragmented and immature.
- Future progress hinges on system-level integration, rigorous real-world validation, and alignment with operational emergency workflows.
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