Preferences for Smartphone Versus In-Person Delivery of Contingency Management: A Web-Based Survey of Australians Who Use Methamphetamine.

Disasters pose a persistent and evolving threat to health care systems worldwide, including natural hazards, technological incidents, infectious disease outbreaks, and mass casualty events. Disaster medicine (DM) is a multidisciplinary field concerned with the health sector’s roles across mitigation, preparedness, response, recovery, and resilience during disasters while maintaining continuity of essential health services [,]. AI, including machine learning, deep learning, natural language processing, optimization, and rule-based decision support, has emerged as a promising set of methods for addressing these challenges. In this review, AI was operationally defined to include data-driven prediction, classification, detection, optimization, and decision-support approaches applied to disaster-related or emergency health contexts. AI-driven systems have shown potential to provide early warning, surge prediction, triage support, resource optimization, and real-time decision support across health care and emergency settings []. Despite growing interest, the application of AI in DM remains fragmented. Existing studies vary widely in scope, methodology, and implementation context, and many rely on retrospective datasets, simulations, or single-system analyses [-]. Prospective, real-time, and health care system–integrated applications appear to be limited. A comprehensive mapping of the current evidence is therefore needed to identify research gaps, implementation barriers, and priorities for future research [].