%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 27
%N 
%P e67318
%T Bridging Data Gaps in Emergency Care: The NIGHTINGALE Project and the Future of AI in Mass Casualty Management
%A ,
%A Caviglia,Marta
%+ Center for Research and Training in Disaster Medicine, Humanitarian Aid and Global Health (CRIMEDIM), Università del Piemonte Orientale, Via Lanino 1, Novara, 28100, Italy, 39 0321 660 620, marta.caviglia@med.uniupo.it
%K AI
%K technology
%K mass casualty incident
%K incident management
%K artificial intelligence
%K emergency care
%K MCI
%K data gaps
%K tool
%D 2025
%7 10.4.2025
%9 Viewpoint
%J J Med Internet Res
%G English
%X In the context of mass casualty incident (MCI) management, artificial intelligence (AI) represents a promising future, offering potential improvements in processes such as triage, decision support, and resource optimization. However, the effectiveness of AI is heavily reliant on the availability of quality data. Currently, MCI data are scarce and difficult to obtain, as critical information regarding patient demographics, vital signs, and treatment responses is often missing or incomplete, particularly in the prehospital setting. Although the NIGHTINGALE (Novel Integrated Toolkit for Enhanced Pre-Hospital Life Support and Triage in Challenging and Large Emergencies) project is actively addressing these challenges by developing a comprehensive toolkit designed to support first responders and enhance data collection during MCIs, significant work remains to ensure the tools are fully operational and can effectively integrate continuous monitoring and data management. To further advance these efforts, we provide a series of recommendation, advocating for increased European Union funding to facilitate the generation of diverse and high-quality datasets essential for training AI models, including the application of transfer learning and the development of tools supporting data collection during MCIs, while fostering continuous collaboration between end users and technical developers. By securing these resources, we can enhance the efficiency and adaptability of AI applications in emergency care, bridging the current data gaps and ultimately improving outcomes during critical situations.
%R 10.2196/67318
%U https://www.jmir.org/2025/1/e67318
%U https://doi.org/10.2196/67318