The digital transformation of clinical teaching: a review of AI and data-driven quality assurance systems in medical education

Abstract

Background The use of artificial intelligence (AI) and other data-driven technologies is changing how medical education and clinical teaching are conducted. The traditional model of learning will not allow us to keep up with the sheer amount of new knowledge being produced, the move to competency-based education, or the need for learning that can be scaled and tailored to fit individuals. To examine how AI is being used in clinical education and evaluate its impact on teaching, learning, assessment, and quality assurance. Methods A review of the literature was conducted to examine the applications of AI in clinical education, including adaptive learning, intelligent tutoring, virtual patients, simulation, automated assessment, and data-driven quality assurance. Results AI has been shown to enhance the engagement of learners, create flexible learning opportunities, provide timely feedback for information on a learner's progress, and provide ongoing objective assessment of clinical competency. The use of learning analytics and predictive modeling enables institutions to monitor the progression of the learner, identify learners at risk, optimize the design of their curriculum, and make evidence-based academic decisions. Future innovations in medical training environments could be possible through the use of digital twins, immersive simulation, and explainable AI. However, there remain significant challenges to the widespread implementation of these technologies, such as bias in algorithms, lack of transparency, issues related to data privacy, unregulated use, and the limitations of current systems. Research to date has demonstrated great variation across studies and has not provided longitudinal data to support any conclusions about the long-term impact of using AI tools. Conclusions Despite the great advantages offered by using AI-based technologies in clinical education, Successful integration will require robust governance frameworks, faculty development, equitable access, rigorous validation, and continuous evaluation to ensure ethical, effective, and sustainable implementation.