JMIR Medical Education

Large language models (LLMs) are increasingly used to generate case-based materials for health professions education (HPE). Previous reviews have examined broad applications in HPE or focused on clinical cases, but none have comprehensively synthesized the generation and evaluation of diverse case-based materials.

Large language model–based virtual-patient systems score history-taking practice, but whether scores correspond to independently scored examination performance remains untested. Existing evaluations emphasize internal platform metrics or simulated-patient fidelity, leaving criterion-related evidence limited.

Mobile learning apps are increasingly used in medical education, but cross-national evidence on their longitudinal development and rating-based satisfaction patterns remains limited.

Short-answer questions (SAQs) effectively assess higher-order reasoning skills in medical education but face challenges in scalability due to time-intensive manual grading and concerns about interrater reliability. Recent advances in large language models (LLMs) may offer a solution through automated assessment.

Large language models (LLMs) achieve high scores on health-professions examinations. Whether these scores support the same interpretations as human scores remains uncertain. Spain uses parallel multiple-choice examinations for entry to specialty training in medicine (Médico Interno Residente [MIR]), nursing (Enfermero Interno Residente [EIR]), psychology (Psicólogo Interno Residente [PIR]), and pharmacy (Farmacéutico Interno Residente [FIR]).

Virtual patients (VPs) are widely used in medical education, but creating high-quality cases requires clinical expertise, iterative revision, and alignment with educational objectives. Generative AI may support VP authoring, but direct comparisons between AI-generated and clinician-developed cases delivered within the same educational setting remain limited.

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.