Practical and Legal Aspects of Applying Artificial Intelligence in Emergency Medicine

This narrative review explores current applications of artificial intelligence (AI) in emergency medicine, critically evaluates the supporting evidence, and discusses the ethical, legal, and regulatory challenges surrounding its integration into clinical practice. Peer-reviewed literature and recent systematic reviews on AI applications in emergency medicine were analyzed using a structured narrative approach. AI-driven operational forecasting, predictive modeling for patient outcomes, diagnostic support, and AI-assisted triage systems are among the domains evaluated. AI models, such as neural networks and gradient boosting machines, have demonstrated superiority over traditional triage tools in forecasting outcomes like in-hospital mortality and intensive care unit admission. AI in diagnostics has enhanced point-of-care ultrasound analysis, sepsis detection, and electrocardiogram interpretation. Operationally, AI makes it possible to predict patient volume, emergency department crowding, and resource allocation in real time. Despite these developments, there are still few prospective clinical trials confirming better patient outcomes. Algorithmic bias, a lack of transparency, automation bias, and restrictions on generalizability across clinical settings are among the main issues. Emerging regulatory frameworks like the European Union AI act and ethical and legal frameworks like the General Data Protection Regulation and Health Insurance Portability and Accountability Act are essential for directing the responsible use of AI. AI has significant potential to improve the provision of emergency care. However, ethical protections, legal compliance, integration with clinical workflows, and thorough external validation are necessary for responsible implementation. To guarantee the safe and fair implementation of AI in emergency medicine, future initiatives must concentrate on explainable AI, multicenter prospective research, and stakeholder collaboration.