JMIR Medical Education

There is growing concern that artificial intelligence (AI) may diminish the quality of human relationships. However, in a context of widespread social importance (empathetic conversations between doctors and patients), AI can actually improve human conversational skills, potentially enhancing professional relationships. Recent advances in AI allow for realistically role-prompted counterparts for practicing professional conversations, enabling relational learning without the need for human counterparts.

According to the World Health Organization, education and awareness are essential components of public health promotion strategies. In the context of rare diseases (RDs), these actions are particularly critical because of persistent stigma, fragmented knowledge, and the frequent absence of consolidated clinical and organizational protocols. These gaps often result in inappropriate referrals, inefficient care pathways, unnecessary procedures, and delays in diagnosis, negatively affecting health outcomes and quality of life.

Personalized feedback improves the clinical pediatric behavior guidance performance of students but is prohibitively time-consuming to provide. Large language models (LLMs) can automate the process of evaluating clinical sessions but are limited to text-only input and consistency issues.

Artificial intelligence (AI) is increasingly transforming health care through improvements in diagnosis, predictive analytics, and workflow optimization. However, there remains a significant gap in AI training within UK medical education, leaving future clinicians underprepared for AI-driven health care environments. This viewpoint paper investigated global best practices for AI integration into medical education and proposes a structured framework for embedding AI into the UK medical curriculum. It aimed to assess current attitudes, highlight existing knowledge gaps, and recommend practical implementation strategies. An analysis of international case studies (eg, Stanford University, the University of Toronto, and Chinese University of Hong Kong) was conducted alongside a review of teaching methodologies, stakeholder perspectives, and UK-based surveys to identify core competencies and challenges in AI education. Effective integration strategies include the use of AI-powered simulations, interdisciplinary collaboration, elective modules, and faculty training. Major barriers include lack of AI-literate educators, insufficient ethical training, and limited infrastructure. Knowledge gaps persist among students and faculty in areas such as algorithmic bias, AI ethics, and clinical decision-making. To meet the demands of modern health care, the UK medical curriculum must adopt comprehensive AI training. This includes practical exposure, ethical awareness, and stakeholder engagement. Proactive reform will ensure that graduates are equipped to critically and ethically apply AI tools in clinical practice.

Multiple mini interviews (MMIs) are widely used in medical school admissions to assess applicants’ nonacademic attributes in a structured and reliable manner. However, the development of high-quality MMI stations is resource intensive and dependent on expert input.

For novice anatomy learners, studying human anatomy using textbooks, 2D learning materials, and static anatomical models frequently causes challenges in understanding complex anatomical structures. Since access to dissected human donor bodies is limited in many premedical programs, researchers are concerned with exploring novel supplementary approaches to anatomy learning. This research explores the effectiveness of an augmented reality (AR) app in enhancing the anatomy learning experiences of premedical students.

Clinical documentation is a foundational skill in medicine, developed during training and required in everyday practice. Historically, the chart note functioned as a clinician-centered cognitive tool for reasoning, teaching, and communication but has evolved into a multipurpose document shaped by administrative, regulatory, and financial demands, and is increasingly experienced as burdensome. The electronic health record, intended to improve efficiency, has introduced additional complexity and workflow strain, contributing to clinician burnout. Ambient artificial intelligence (AI) scribe technologies are rapidly being adopted to address these challenges, yet their implementation has outpaced evidence regarding their impact on learning, cognition, and clinical reasoning. We raise questions regarding the underexplored consequences of AI-assisted documentation, particularly cognitive off-loading and the potential for de-skilling, echoing historical concerns surrounding earlier cognitive technologies that externalized thought. We propose a practical framework that re-centers clinical documentation around four core aims: supporting clinical reasoning (“note to self”), facilitating communication (“note to others”), meeting medicolegal and billing requirements, and enhancing patient education in the era of open notes. Incorporating this framework into training may promote more intentional documentation practices before routine reliance on AI. We advocate for reframing the chart note to support clinician development and preserve its role in high-quality, patient-centered care.

Artificial intelligence (AI) is reshaping clinical practice and redefining the competencies future physicians will need. International bodies, such as the Association of American Medical Colleges, have called for structured AI training in medical curricula. Despite growing international consensus, no systematic nationwide evaluation had been conducted in Spain prior to this study.

Social media apps are widely used by health care professionals despite security and regulatory risks. Identifying factors associated with this use is important for developing effective risk-reduction strategies.

The Kingdom of Saudi Arabia and the Kingdom of Bahrain are transforming their health care systems toward more self-sustained, autonomous systems. Effective leadership at all management levels, particularly middle management, is critical for operational success.

Applicants participating in the Residency Match generally submit a photograph through the Electronic Residency Application Service (ERAS). Studies demonstrate that subjectively more attractive applicants are more likely to succeed during job recruitment, including a paper related to the Residency Match.

Artificial intelligence (AI) is rapidly reshaping clinical education by embedding assessment and feedback into everyday learning activities. Medical students can now use machine learning dashboards, generative AI, large language models, and emerging agentic systems to practice clinical reasoning, communication, and procedural skills while receiving individualized feedback within seconds. However, the availability of more data and more feedback does not necessarily produce better learning. This Viewpoint is intended for clinical educators, assessment leaders, curriculum committees, faculty developers, and institutional leaders who must decide how AI should be used in formative activities without reducing education to automated scoring. AI-assisted formative assessment is defined in this paper as the intentional use of AI tools to generate, organize, and support interpretation of performance information for learning rather than grading. Its distinctive contribution lies in the scale, adaptivity, conversational simulation, pattern detection, and possible autonomy of AI systems. However, AI outputs become formative only when learners and educators interpret them critically, judge their trustworthiness, and translate them into a small number of focused follow-on learning actions. This paper synthesizes the current evidence base while noting that much of it remains early, heterogeneous, and concentrated in short-term or single-setting studies. It examines key risks, including hallucination, automation bias, epistemic overtrust, hidden curricular effects, and broader concerns related to professional identity, power asymmetries, data privacy, and inequitable access. It also presents context-specific implementation examples for preclinical case-based learning, communication and objective structured clinical examination preparation, procedural skill laboratories, clerkship learning, and programmatic assessment portfolios, together with practical implications for faculty development, institutional governance, and phased local implementation. As a Viewpoint rather than an empirical study or systematic review, the framework and examples should be interpreted as evidence-informed design propositions that require local evaluation and validation. Overall, the value of AI-assisted formative assessment depends less on the volume of AI-generated feedback than on educational designs that preserve learner agency, professional judgment, and human accountability.