Artificial intelligence in emergency medicine education: A narrative review.
TL;DR
This narrative review seeks to synthesize current literature on the applications, benefits, limitations, and future directions of AI in emergency medicine education across undergraduate, graduate, and continuing medical education for medical educators.
Abstract
INTRODUCTION Artificial intelligence (AI) has rapidly emerged as a leading technology driving advances in emergency medicine, particularly in medical education.
Objective
This narrative review seeks to synthesize current literature on the applications, benefits, limitations, and future directions of AI in emergency medicine education across undergraduate, graduate, and continuing medical education for medical educators.
Discussion
AI tools such as large language models are rapidly being implemented in educational strategies such as case-based learning, simulation, self-directed learning, gamification, and on-shift learning. These tools can save time and streamline the processes of developing curricula, assessing learners, and giving feedback. Key limitations of this emerging technology specific to emergency medicine include potential stunting of learners' critical thinking, a lack of faculty training, concerns about content accuracy, the potential for bias in AI-generated educational materials, inequitable access to AI tools, and privacy and legal concerns. Emergency medicine educators and trainees alike must ensure that they are co-creators in the AI-augmented educational environment, carefully balancing the strategic advantages of AI with its limitations.
Conclusions
AI tools hold enormous promise for saving time and streamlining educational endeavors in emergency medicine, with the potential to customize education to learner needs. Clinician educators must exercise caution and maintain AI literacy in order to deploy these tools safely and effectively.