Editorial: Digital innovations in disaster response: bridging gaps and saving lives

Dynamic Patient and Situation Simulation in the Context of Mass Casualty Incidents -Insights from the Research Projects D2PuLs and D2PuLs PRO builds directly on this approach by presenting a modular digital training environment for mass-casualty incidents. Dynamic patient models, which react physiologically to interventions, are linked with situation and manikin simulators so that complete rescue chains-from prehospital triage to in-hospital trauma carecan be trained. The study demonstrates that such digital simulations can supplement resource-intensive live exercises and generate detailed data for debriefing and quality improvement.The systematic review Extended Reality Technology for Emergency Medical Service Training: Systematic Review adds an evidence-synthesis perspective on immersive training tools. The review reports that virtual, augmented, and mixed-reality applications can improve engagement, perceived realism, and skill acquisition in emergency medical services, especially for rare or high-risk scenarios. At the same time, it highlights methodological limitations of existing studies and practical barriers such as ergonomic issues, user fatigue, and technical instability. Taken together, these three articles support the role of simulation and extended reality as important elements of disaster preparedness, while emphasising the need for structured curricular integration and robust evaluation.A second set of contributions focuses on artificial intelligence (AI) and data-driven decision support. Applications of Artificial Intelligence-Guided Clinical Decision Support in Disaster Medicine: An International Delphi Study explores, using a Delphi process, which AI functions are prioritised by disaster medicine experts. The study finds broad agreement that AI is particularly useful for improving situational awareness and logistics-such as estimating affected populations, supporting hazard vulnerability analysis, allocating scarce resources, and optimising patient distribution-whereas there is significantly more caution regarding AI involvement in ethically sensitive triage or end-of-life decisions. These results provide important guidance for the design of AI systems that align with practitioner expectations.Anomaly detection and early risk identification in digital disaster response-based on deep learning in public health proposes a deep-learning framework to identify aberrant patterns in health-related data streams. By combining temporal and contextual modelling, the approach improves detection performance and reduces false alarms in comparison with traditional methods, illustrating how AI can support early recognition of emerging threats in digital operations centres. Filling the gap: Artificial Intelligence-driven One Health integration to strengthen pandemic preparedness in resource-limited settings extends the focus to One Health. This article discusses AI-enabled architectures that integrate human, animal, and environmental data for improved surveillance in low-and middle-income countries, and emphasises governance, capacity building, and offline-capable, context-sensitive tools as prerequisites for effective deployment. Finally, The Information Challenge in Public Health Crises: A Study on the reliability and readability of information provided by large language model for thunderstorm asthma examines large language models as direct information sources in crises. By assessing reliability and readability of responses to questions on thunderstorm asthma, the study shows that, although medically relevant content can often be generated, readability frequently exceeds recommended levels for lay audiences and quality differs between systems. The authors conclude that current models should not be used as unsupervised tools for public crisis communication and that professional review and adaptation to target groups are essential.Overall, the eleven articles in this Research Topic demonstrate that digital innovations already contribute to multiple core domains of disaster medicine: competence development through simulation and extended reality, AI-supported situational awareness and One Health surveillance, new service-delivery models via telemedicine and drones, and enhanced information processing using sentiment analysis, named entity recognition, and large language models. At the same time, they consistently show that technological sophistication alone is insufficient. Acceptance by professionals, integration into organisational structures, rigorous evaluation in real-world environments, ethical and legal governance, and a strong focus on equity-particularly in resource-limited settings-are decisive for whether digital tools will truly bridge gaps and improve outcomes in disasters.