JMIR Medical Education

The fundamental goal of medical education is to develop physicians who can independently provide safe and effective patient care. This requires learners to acquire not only knowledge but also meaningful experience to progress along the continuum from novice to expert. In the last several decades, however, medical education has undergone numerous transformations that have resulted in decreased clinical experience for medical students and residents alike. These transformations include the increasing complexity of health care delivery in the inpatient and outpatient settings; duty hour restrictions; and the explosion of digital technologies, including the electronic health record and AI. Additionally, the introduction and broad acceptance of competency-based medical education, which is focused on demonstrable behaviors rather than the time spent in training, risks exacerbating this loss of meaningful clinical experience if implemented without attention to trainees’ need for clinical volume, variation, and redundancy. While these changes in the number and intensity of patient encounters in educational programs are multifactorial in origin, when combined, they jeopardize the acquisition of experience, the development of expertise, and the resulting quality of patient care. In this Viewpoint, our aim is to discuss the importance of meaningful experience in the development of expertise; highlight threats to its achievement resulting from the intersection of 3 transformations affecting medical training (the increased complexity of 21st-century health care systems, expansion of the electronic medical record and AI, and flawed understanding of the full implications of competency-based medical education as presently implemented); and present solutions for medical educators, academic institutions, and accrediting bodies. Our views are based on our perspectives and observations, as well as insights from the literature.

Despite the increasing use of liver contrast-enhanced ultrasound (CEUS), no standardized, organ-specific tool exists for assessing trainees’ cognitive competency. Existing instruments lack multidimensional evaluation and external validation, which limit training quality and patient safety.

AI is reshaping the landscape of medical education, yet the widespread adoption of general-purpose AI tools in student learning raises substantial concerns about curricular alignment, information accuracy, and the absence of pedagogical scaffolding.

Generative AI (GenAI) expands possibilities for content generation, feedback, personalization, and instructional support in higher education. Its growing use also challenges established assumptions about teaching, assessment, professional expertise, academic integrity, and educational responsibility. Although research has examined technology adoption and AI competencies, less is known about how educators experience and interpret the pedagogical transformation associated with GenAI in everyday teaching.

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.