Application of Artificial Intelligence in Advanced Training and Education of Emergency Medicine Doctors: A Narrative Review

Abstract

Emergency medicine (EM) demands continuous adaptation and refinement of training methodologies to equip healthcare professionals with the expertise to effectively manage complex and time-sensitive patient presentations. Artificial intelligence (AI), with its remarkable ability to process vast amounts of data, identify patterns, and make predictions, holds immense promise for enhancing the advanced training and education of EM physicians. This narrative review aims to discuss the potential of AI in transforming EM training and highlight the specific applications of AI in personalized learning, realistic simulations, data-driven decision support, and adaptive assessment, along with further exploring the benefits and challenges of AI-powered EM training. A comprehensive literature search was conducted using PubMed, MEDLINE, and Google Scholar to identify relevant studies focusing on AI applications in EM and EM training. The search terms included “artificial intelligence”, “emergency medicine”, “training”, “education”, “personalized learning”, “simulations”, “decision support”, and “assessment. Articles published in the past ten years were prioritized to ensure the inclusion of current advancements in the field. AI offers a plethora of opportunities to revolutionize EM training, including the following: Personalized learning: AI-powered systems can tailor educational content and pace to individual trainees’ needs, ensuring optimal instruction and knowledge acquisition. Realistic simulations: AI-powered simulations provide immersive experiences for trainees to practice clinical decision making under simulated pressure. Data-driven decision support: AI-powered systems analyze vast amounts of data to provide trainees with real-time recommendations and insights for informed clinical decisions. Adaptive assessment: AI-powered tools assess trainee progress dynamically, providing personalized feedback and identifying areas for improvement. Conclusions: AI integration into EM training holds immense promise for enhancing trainee learning and improving patient outcomes. By embracing AI, we can cultivate a new generation of EM physicians equipped to meet the ever-changing demands of this critical medical specialty.