JMIR Medical Education

Entering clinical training, dental students must learn to read tooth-centered electronic dental records, but limited teaching time in patient-centered clinics can leave gaps in chart-reading literacy. Multimodal large language models (LLMs) that process dental record images may offer scalable educational support, yet their performance has not been benchmarked.

Generative AI tools became widely available to the public in November 2022. The extent to which these tools have been used by medical school applicants during the admissions process is unknown.

High-quality problem-based learning (PBL) during internship is resource-intensive and difficult to scale without consistent facilitation. Although generative AI is increasingly used in health professions education, many applications remain on-demand answer tools that may not reproduce core PBL processes.

Large language model (LLM)–based AI teaching agents are increasingly used in medical education, yet their pedagogical quality is typically judged by platform-generated scores whose scoring criteria are undisclosed and may not reflect the teaching quality of the agent.

The Medical College Admission Test (MCAT) has been central to medical school admissions in North America, though its necessity in holistic selection processes remains debated. The COVID-19 pandemic’s suspension of MCAT testing sessions allowed institutions to explore alternative admission criteria. Additionally, global challenges in test administration underscore the vulnerability of systems dependent on a single standardized test.

Index case testing (ICT) is an effective strategy for HIV case finding, but implementation in low- and middle-income countries (LMICs) is often limited by cost and logistical challenges. Traditional ICT training—centralized and in-person—is costly, disrupts service delivery, and varies in quality.

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Communication during crisis management involves not only the exchange of clinical information but also the expression and regulation of emotional tone and sentiment, which may reflect clinician performance during a critical incident. In simulation-based medical education, these emotional dynamics are rarely measured objectively, limiting the ability to capture aspects of performance relevant to competency assessment.

Spanish-speaking patients who are lesbian, gay, bisexual, transgender, queer, and other minority sexual orientations and gender identities (LGBTQ+) may encounter overlapping barriers in clinical care related to language discordance, misgendering, heteronormative questioning, and culturally unresponsive communication. These barriers can undermine trust, limit disclosure, and negatively affect care experiences and continuity of care. At the same time, medical Spanish curricula in the United States remain highly variable in content, structure, and assessment, and inclusive, bias-aware communication has not been consistently integrated into language training for health professions learners. Our prior qualitative work with Latinx, Hispanic, and Spanish-origin LGBTQ+ adults identified communication priorities directly relevant to educational design, including respectful forms of address, open-ended relationship language, broader recognition of family structures, and reduced reliance on binary or heteronormative assumptions. Our prior curricular work has also shown that structured medical Spanish instruction using self-study, faculty-led teaching, peer support, standardized patient encounters, and performance-based assessment can be implemented in undergraduate medical education. This tutorial presents a patient-informed framework for developing an inclusive medical Spanish module to address bias in clinical communication. Rather than reporting a new intervention study, it synthesizes prior qualitative findings and prior curricular experience to provide practical guidance for educators. The framework describes how to translate patient-reported communication priorities into curricular design principles, learning objectives, technology-enhanced preparatory activities, standardized patient and live-practice components, feedback and assessment strategies, and implementation planning. It argues that inclusive communication should not be treated as optional cultural content or as a narrow language add-on but as a core component of competent clinical performance in Spanish-language care. This tutorial is intended for medical educators, language educators, course directors, and simulation teams seeking to strengthen language-concordant and bias-aware communication training in medical Spanish. By linking patient-informed findings to concrete educational design choices, the framework offers a practical model for integrating inclusive communication throughout instruction, simulation, and assessment. A patient-informed approach of this kind may help health professions programs move beyond general statements about inclusion and toward more deliberate preparation of learners to communicate respectfully and effectively with Spanish-speaking LGBTQ+ patients.

This paper critically assesses the role of generative AI in anatomical illustration, identifying fundamental barriers that currently preclude AI from replacing human medical illustrators. Despite the promise of unprecedented efficiency, contemporary models exhibit persistent anatomical inaccuracies and “hallucinations” of nonexistent structures—flaws stemming from statistical pattern-matching rather than genuine anatomical understanding. These systems further lack pedagogical intent, clinical context, and the capacity for deliberate visual judgment, while raising unresolved ethical and copyright concerns regarding training data. Although a specialized AI for this purpose is theoretically feasible, its development as a standalone goal remains economically nonviable given the niche nature of the profession. Rather than replacing human illustrators, AI’s future role will be augmentative, with the requisite anatomical intelligence likely emerging as a byproduct of broader advances in clinical applications such as surgical planning and personalized medicine. For AI-generated imagery to become educationally and clinically reliable, it will require rigorous human supervision, curated gold standard datasets, and a foundation of genuine anatomical comprehension.

Generative AI is driving medical education from digital support toward intelligent, interactive learning environments. The cultivation of doctor-patient communication skills requires not only technical proficiency but also communication competence, emotional sensitivity, and ethical judgment. This paper proposes a conceptual framework for AI-driven digital standardized patients (AI-SPs) to provide new approaches for communication training, humanistic education, and emotional engagement in medical curricula. This viewpoint integrates research findings from educational technology and medical education, elaborating the framework from 4 dimensions: system architecture, multimodal interaction, personality modeling, and ethical considerations. AI-SPs can provide adaptive, emotionally responsive interactions in repeatable, controllable simulated scenarios, enabling learners to experience diverse patient characteristics and clinical situations. The proposed “future learning” framework emphasizes personalization, contextualization, and reflective learning. During implementation, attention must be paid to data privacy, algorithmic bias, and human supervision. AI-SPs represent an extension of the traditional standardized patient model. They facilitate human-AI collaborative learning, support the cultivation of empathy, and provide a new pathway for the appropriate application of generative AI in medical education.

Simulation-based medical education is essential for improving patient safety. In virtual reality (VR)–based simulation, immersion is primarily generated through visual and auditory cues, while other sensory modalities are typically absent. This sensory limitation may reduce the emergence of authentic safety-relevant behaviors. Olfaction plays an important role in clinical reasoning, risk perception, and self-protective behavior and is closely linked to memory and emotion. Although olfactory cues have been shown to influence hand hygiene behavior in real or simulated environments, their targeted integration into fully immersive VR-based medical simulation has not been systematically examined.