Artificial intelligence in health sciences education: A mini-review of clinical reasoning development and educator perceptions

Abstract

Clinical reasoning forms the basis of practice in the health sciences, enabling professionals to collect and interpret patient data to support sound clinical decisions. Artificial intelligence (AI) and machine learning (ML) have emerged as strong tools, revolutionizing diagnosis, personalizing treatment, and enhancing therapeutic outcomes. However, very little research has been conducted concerning health sciences educators’ perceptions regarding AI and its role in clinical reasoning skill development. This mini-review examines AI’s growing prevalence in health sciences education, as well as the challenges and opportunities it presents. A systematic search of the scholarly literature identified studies related to AI applications in the teaching and learning of clinical reasoning. A number of recurring themes emerged from these analyses, including the potential for AI to transform the training of clinical skills, the ethical implications associated with AI implementation, contributions from the field of ML to clinical data analysis, and innovations in educational assessment. Thus, these findings highlight the need for ethical frameworks, interdisciplinary collaborations, and sustained research to enhance the benefits of AI. Proper integration of AI in health sciences education would significantly assist students in improving their clinical reasoning skills, enabling them to become competent practitioners so that, ultimately, patients may benefit in an increasingly digital health environment.

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