ARTIFICIAL INTELLIGENCE IN MEDICAL EDUCATION AND CLINICAL PRACTICE: A NARRATIVE REVIEW
TL;DR
AI delivers its greatest value when deployed as a collaborative instrument that augments rather than replaces clinical judgment, which depends on sustained investment in digital literacy, explainable systems, clinician involvement in design, and equitable institutional infrastructure.
Abstract
Background: Artificial intelligence is rapidly reshaping medicine, influencing clinicians’ training and patient care. Despite growing enthusiasm for AI-enabled diagnostics and digital learning tools, integration of these technologies into medical education and clinical practice remains uneven. Objective: This narrative review synthesises recent literature on the role of AI in medical education and clinical practice, mapping current applications, stakeholder perceptions, and the conditions that determine successful adoption. Methods: Relevant articles were identified through structured searches of PubMed, Scopus, and EMBASE, prioritising literature from the last decade. Empirical studies, methodological reviews, and authoritative commentaries addressing AI, machine learning, or digital health technologies in clinical or educational settings were included and organised thematically. Findings: AI-powered tools are embedded across medical training, spanning augmented and virtual reality simulations, bedside mobile learning, and a range of clinical specialties, with performance matching expert levels on narrowly defined diagnostic tasks. Stakeholder attitudes are positive but conditional: medical students expect digital health technology to feature in their curricula yet many consider their own competencies inadequate, and very few physicians-in-training report familiarity with AI. Patients express willingness to engage with AI for self-management while preferring human contact in sensitive clinical domains. Persistent barriers remain, including limited availability of curated data, algorithmic bias, difficulties reproducing published models, and underdeveloped regulatory and workflow structures. Conclusion: AI delivers its greatest value when deployed as a collaborative instrument that augments rather than replaces clinical judgment. Realising this potential depends on sustained investment in digital literacy, explainable systems, clinician involvement in design, and equitable institutional infrastructure.
