JMIR Medical Education

Generative AI (GenAI) is increasingly used during medical training, including clerkships in which learning takes place alongside supervised patient care. Its educational value may depend on how students interpret, verify, and regulate AI-generated output.

AI is quickly becoming a foundational component of many clinical workflows. New tools are continually evolving to enable a host of functionalities, from ambient documentation to enhanced decision support, assistive diagnostic tools, risk prediction, patient triage, and patient communication. As a result, medical education has begun to address issues of AI literacy and the ethics of its use, including some work on competency frameworks, yet comparatively little attention has been given to the relational competencies clinicians require when AI increasingly mediates the patient encounter. Existing AI competency frameworks emphasize technical literacy, evaluation skills, and sometimes ethical awareness, yet the literature suggests that AI tools alter the structure of the patient-clinician relationship itself, raising questions about trust, transparency, empathic presence, and accountable human judgment. The empathy education literature has established that empathic communication is both teachable and assessable, and the AI competency literature has established that clinicians require new knowledge and skills for AI-enabled practice. These 2 bodies of work, however, remain insufficiently integrated. Medical education currently lacks a coherent competency framework for the relational work clinicians must perform when AI tools shape clinical information, communication, documentation, diagnosis, risk prediction, or treatment recommendations. This viewpoint proposes AI-mediated relational competence as a distinct construct for medical education, operationalized through 6 observable subcompetencies: empathic presence, trustworthiness, transparency in AI use, relational judgment, accountable human decision-making, and equity-aware trust-building. A complementary 5-level curriculum maturity model is introduced for evaluating whether medical schools move beyond curricular mention to systematic, competency-based implementation. Together, the construct and maturity framework are intended to provide the conceptual foundation for subsequent empirical work, including online curriculum assessment, national survey research, expert consensus validation, and competency-based assessment instrument development.

Blended learning is common in health professions education, but aggregate comparisons may obscure differences among knowledge, procedural skill, and integrated performance. Whether these domains differ after program-level changes under a common assessment system remains uncertain.

Health care providers (HCPs) are key agents in supporting health behavior (eg, smoking and physical activity) change in patients. Toolkits have previously been used to guide HCPs in care and have shown effectiveness in improving clinical outcomes. However, there is a lack of understanding of how toolkits should be developed to best support HCPs.

AI is increasingly relevant to health care and health professional education, but AI-related knowledge, perceptions, and training needs among midwifery students remain underdocumented. Evidence is needed to inform midwifery curricula and to clarify how students understand and operationalize AI in training and placements.

Clinical summary statements convert unstructured patient information into a concise format. They are widely used in virtual patients (VPs) to train medical students in diagnostic reasoning. Most VP systems provide little individualized feedback on the quality of learners’ summaries, and earlier automated approaches have required complex natural language processing, limiting their scalability. Large language models (LLMs) may offer a more flexible, rubric-guided way to score summary statements and support feedback. Still, their test-retest reliability and concordance with expert ratings in this context remain unclear.

Student engagement reforms have increased active learning in higher education. However, visible participation does not always require students to rehearse the spoken, interactive forms of reasoning central to professional practice, such as explaining one’s thinking, responding to challenges, building on others’ ideas, and working through uncertainty in real time. We describe this gap as an engagement blind spot. We define audible reasoning as the deliberate externalization of disciplinary or professional reasoning through spoken interaction so that its assumptions, evidence, uncertainty, and justification can be examined, challenged, revised, and developed collaboratively. It is particularly relevant in health professions education and also applies to laboratory-based and scientific disciplines where students must communicate their reasoning under conditions of uncertainty, scrutiny, and collaboration. Digital, hybrid, asynchronous, and AI-enabled learning environments sharpen this blind spot. Shared documents, online quizzes, discussion boards, and generative AI tools can support participation and access; however, they can also allow students to engage without speaking or to produce polished answers without making the underlying reasoning visible. In this context, educators need to ask not only whether students are engaged but also what behaviors they are rehearsing and whether their reasoning is available for examination and development. This tutorial offers 12 practical, evidence-informed tips developed iteratively from recurring challenges encountered in teaching, curriculum design, and faculty development and refined against selected literature. The tips are organized around 4 clusters covering the design of activities that require audible reasoning, the elicitation of reasoning in practice, the development of reasoning through listening and response, and the preservation of audible reasoning in digital and AI-enhanced environments. Across these 4 clusters, we suggest small but intentional shifts that help educators move from silent, constructive tasks toward audible, dialogic, and professionally relevant participation. These include designing tasks around uncertainty, creating low-stakes opportunities for spoken reasoning, using digital tools to surface and organize contributions for further discussion, structuring active listening and peer feedback, reviewing group dialogue formatively, and using conversational AI as a rehearsal partner rather than an authority. These strategies require attention to psychological safety, cultural responsiveness, accessibility, equity, privacy, data governance, and ongoing monitoring. Audible reasoning is not synonymous with speaking more, volunteering more frequently, or sounding fluent. Designing for audible reasoning is not simply an engagement strategy. It is a way of helping students practice the communicative and collaborative behaviors needed to explain, defend, question, and revise human judgment in clinical, scientific, and other professional contexts.

Serious illness communication (SIC) is a critical skill in medical education, especially in end-of-life care, yet clinicians often lack sufficient opportunities for deliberate practice. Standardized patients (SPs) remain the gold standard for SIC training, but they are limited by cost, availability, and consistency. To support wider access to SIC training, scalable alternatives are needed.

Generative AI, including large language models (LLMs), may enable personalized learning in medical education. In otolaryngology–head and neck surgery (ear, nose, and throat [ENT]) education, AI may support knowledge retrieval, adaptive assessment, feedback, and scalable learning, but evidence remains fragmented.

Urology education must prepare learners to integrate anatomical visualization, diagnostic reasoning, technical performance, communication, and ethical judgment, often under restricted working hours and limited access to high-fidelity cases. This viewpoint argues that large language models (LLMs) and metaverse-related technologies should be integrated as complementary, supervised educational tools rather than treated as autonomous replacements for educators or clinical experience. LLMs can support cognitive apprenticeship, documentation practice, assessment, and communication simulations, whereas extended reality, cloud computing, and virtual digital humans can support spatial, procedural, and remote learning. Our aim is to articulate 3 key messages: first, the educational value of these technologies depends on alignment with defined urological competencies; second, implementation should proceed in stages from governed data infrastructure to intelligent interaction and immersive multimodal training; and third, educational effectiveness must be established through prospective evaluations of transfer to clinical performance, not through technical novelty alone. We emphasize the need for rigorous randomized controlled trials, identify requirements for haptic and biomechanical fidelity, and discuss hallucination, bias, privacy, accountability, and deskilling. The responsible future of intelligent urology education will therefore require collaboration among learners, educators, clinicians, engineers, ethicists, institutions, and professional bodies.

Artificial intelligence is rapidly reshaping clinical research, diagnostics, health system operations, and medical education. Frontier generative AI development is increasingly concentrated in private industry, whose computational infrastructure, proprietary datasets, and capital exceed those of most academic institutions. Although not designed exclusively for medicine, these systems increasingly influence clinical applications. This shift raises a question for academic medical centers (AMCs): What role remains when leading AI systems emerge outside universities? This viewpoint examines industry’s growing role in generative AI, clarifies AMCs’ responsibilities, and proposes a framework for AI governance and literacy across undergraduate, graduate, and continuing medical education (CME). Rather than compete with industry on computational scale, AMCs should reinforce functions for which AMCs have distinctive strengths. These include (1) mechanistic inquiry linking algorithmic outputs to biological and pathophysiologic understanding; (2) interdisciplinary synthesis integrating clinical, ethical, legal, and sociotechnical perspectives; (3) independent and methodologically rigorous validation of clinical AI tools; and (4) institutional stewardship of ethical governance for patient populations that are often underrepresented in commercial datasets. As nonprofit institutions training clinicians, AMCs have a distinct role in safeguarding transparency, fairness, and accountability in AI-enabled health care. We position this 4-role framework to refine AI governance and translational medicine approaches, clarifying which responsibilities AMCs should retain while collaborating with industry. The approach includes longitudinal AI literacy training across undergraduate medical education, graduate medical education, and CME, standardized pathways for evaluating clinical AI, stronger conflict-of-interest safeguards, and complementary partnerships with industry and regulators. We also outline educational assessment methods and safety-monitoring processes, including competency-based assessment across undergraduate medical education, graduate medical education, and CME, postdeployment monitoring, and clear escalation triggers. Embedding verification habits and governance into training and practice can help ensure that generative AI supports clinical reasoning while preserving trust, equity, and scientific rigor. This viewpoint addresses academic leaders, curriculum committees, residency directors, faculty developers, and accreditors. Safe clinical AI requires sustained academic leadership.

Simulation-based education is increasingly used in medical training to safely develop clinical skills. However, the perceived relevance of a simulation (“utility value”) to a learner’s future work may influence its effectiveness.