Is it time for AI to take a leading role in mass trauma mental health response?

Machine learning (ML) and artificial intelligence (AI) offer opportunity and risk in mass trauma response, disasters and crisis. This narrative review synthesizes material from our "AI to the Rescue" panel at the inaugural PreAct Mass Trauma conference in June 2025, integrating relevant literature and the authors' expertise. We examine AI approaches beyond large language models (LLMs), including traditional ML and multimodal systems, while grounding the concept of "AI-made disasters" as a necessary third disaster type alongside Human-made and Natural, supported by emerging evidence of AI-caused psychiatric harm. We present the AI Safety Levels for Mental Health (ASL-MH) framework with six levels - from supportive applications, to autonomous packages, to experimental, high-risk systems - positioned as a practical heuristic for graduated risk governance given the nascent regulatory landscape and the demonstrated fragility of voluntary industry safety commitments. Using the Model for Adaptive Response to Complex Cyclical Disasters (MARCCD) framework, we organize AI applications across four phases: Anticipation, Impact, Adaptation, and Growth & Recovery, with attention to core disaster mental health sequelae and the challenge of differentiating normative distress from psychopathology. Recommendations address research/evidence, governance/regulation, training/literacy, and equity/access. Given our presentation involved live demos of AI applications, we have distilled key elements into this review which cannot be directly shown.