Artificial Intelligence in Emergency Medicine: From Innovation to Responsible Integration

Artificial intelligence is increasingly shaping emergency medicine, with applications spanning clinical decision support, triage and diagnostics, workflow and resource optimization, and education. Its potential contribution extends across the entire emergency care continuum, from prehospital and emergency medical services through the emergency department and into interfacility transfer and transitions of care. Effective integration depends on robust data and digital ecosystems, including data quality, interoperability, and integration with electronic health records and monitoring systems. At the same time, moving from algorithmic performance to clinically meaningful evidence remains a central challenge, requiring internal and external validation, real-world evaluation, and comparison with existing standards of care.This Research Topic aims to bring together methodological advances, real-world applications, and critical perspectives on the evaluation, implementation, and responsible integration of AI across the emergency care continuum. Translating technological advances into meaningful improvements in emergency care requires robust clinical evidence, effective integration into complex and time-critical workflows, and careful consideration of human, ethical, legal, and organizational factors. The collection will address AI's role in triage, diagnosis, clinical decision support, and operational efficiency, while also examining evaluation methodology, data infrastructure, human-AI interaction, governance, and the educational needs the technology creates. By covering both the clinical and operational dimensions of AI adoption, the goal is to move the field from isolated technical demonstrations toward evidence-based, responsibly implemented AI in emergency and disaster medicine.We welcome contributions addressing: (1) AI-enabled triage, diagnosis, prognostication, and clinical decision support, including multimodal data integration; (2) AI for emergency care operations, workflow, and resource optimization; (3) AI across the emergency care continuum, from prehospital and EMS to interfacility transfer; (4) data, digital infrastructure, and interoperability; (5) evaluation, validation, and evidence for clinical AI; (6) human-AI interaction and implementation in emergency care; (7) ethics, equity, governance, and legal accountability; and (8) AI and education in emergency medicine. Topic editors: Diana Lungeanu, Alina Petrica, Rocío Pérez de Prado, Ashkan Ebadi Source: https://www.frontiersin.org/research-topics/85283/artificial-intelligence-in-emergency-medicine-from-innovation-to-responsible-integration