
This introductory narrative review has been conducted by the SAPEA literature review team at Cardiff University, at the request of the European Commission’s SAM Secretariat and on behalf of the Emergency Response Coordination Centre (ERCC) at DG ECHO. It synthesises recently published evidence on the use of Artificial Intelligence (AI) in Disaster Risk Management (DRM). The review addresses questions outlined in the Project Concept Note, focusing on opportunities, risks, ethical considerations, and alignment with EU legislation and guidelines. It summarises recent review papers in the academic literature, as well as published reports and papers by organisations in the field. It is designed to complement the SAPEA Rapid Evidence Review Report (2025), The role and use of artificial intelligence for emergency and crisis management.The narrative review examines the relevant literature within the context of crisis and emergency management. As requested, it seeks to: (1) identify current practices and tools that employ AI in emergency preparedness and operational settings; (2) evaluate what is working, emerging, or problematic; and (3) identify evidence gaps and areas for future research and investment. It includes general policy-oriented recommendations on the responsible use of AI in disaster preparedness and response, taken from the literature.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
