ReviewJMIR AI2026
AI in Disaster Medicine: Scoping Review of Methods, Validation, and System Integration.
Review in JMIR AI, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.