JMIR Medical Education最新文献

{"title":"Generative AI for Personalized Learning Platforms in Otolaryngology-Head and Neck Surgery Education: Scoping Review.","authors":"Jiang-Tao Zhong, Zai-Zai Cao, Shui-Hong Zhou","doi":"10.2196/91785","DOIUrl":"10.2196/91785","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Objective: </strong>This scoping review aimed to map generative AI research in otolaryngology education; summarize applications, performance, benefits, and risks; and identify directions for AI-enabled personalized learning.</p><p><strong>Methods: </strong>We included English-language empirical studies, preprints, and technical or developmental evaluations published from 2017 through 2026 with direct relevance to professional ENT education; nonempirical publications and studies without a professional educational focus were excluded. PubMed, Embase, Web of Science, Scopus, ERIC, IEEE Xplore, and CINAHL were searched, supplemented by citation searching. The final search was conducted on July 6, 2026. Data were charted using a standardized form by 2 reviewers and synthesized through a 3-stage inductive thematic analysis, with third reviewer adjudication. No formal critical appraisal or meta-analysis was performed because of study heterogeneity.</p><p><strong>Results: </strong>A total of 42 studies published between 2023 and 2026 were included. Most were performance studies (n=33, 78.6%), followed by system development (n=3, 7.1%), educational intervention studies (n=3, 7.1%), surveys (n=2, 4.8%), and guideline development (n=1, 2.4%). Most studies evaluated question sets or model outputs rather than participants. Applications included knowledge retrieval, revision, board-style question practice, adaptive assessment, AI avatar-delivered instruction, and operative-planning support. Seven major thematic domains were identified: (1) educational support and learning enhancement; (2) learning outcomes, educational effectiveness, and pedagogical value; (3) model performance, accuracy, and variability; (4) assessment, examination readiness, and question-type sensitivity; (5) reliability, safety, and risks; (6) adoption, usability, and implementation barriers; and (7) advanced AI architectures and future educational design. Newer models performed strongly on selected structured knowledge tasks, but performance varied by task, difficulty, modality, and reasoning demands. Evidence remained heterogeneous and benchmarking focused, with only 3 educational interventions and limited evidence for sustained learning, clinical reasoning, or real-world outcomes. Hallucinations, unreliable sourcing, confidently incorrect outputs, and contextual and multimodal reasoning limitations supported supervised rather than autonomous use.</p><p><strong>Conclusions: </strong>By moving beyond reviews centered on AI applications or model performance, this review provides an education-focused synthesis linking generative AI capabi","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"12 ","pages":"e91785"},"PeriodicalIF":13.9,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149225110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating Large Language Models and Metaverse-Related Technologies in Urology Education: Viewpoint.","authors":"Peizhe Li, Huijie Gong, Heyang Liu, Siyu Han, Yiqin Lu, Yongji Yan","doi":"10.2196/93235","DOIUrl":"https://doi.org/10.2196/93235","url":null,"abstract":"<p><strong>Unlabelled: </strong>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.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"12 ","pages":"e93235"},"PeriodicalIF":13.9,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149225130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The New Gravity: How Generative AI Remapped Innovation and What Academia Must Do Next.","authors":"Hossein Akbarialiabad, Ayman Grada, Sancy A Leachman, David Rhew, Alireza Haghighi","doi":"10.2196/89741","DOIUrl":"https://doi.org/10.2196/89741","url":null,"abstract":"<p><strong>Unlabelled: </strong>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.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"12 ","pages":"e89741"},"PeriodicalIF":13.9,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149225142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Curriculum-Aligned Conversational AI to Support Pathology Self-Directed Learning in Preclinical Medical Students: A Crossover Randomized Pilot Study.","authors":"Kasie Roark, Yumin Choi, Elizabeth Hoy, Noel Tufts, Robert Amevor, Rahul Ghosal, Brian Keisler, Leonardo Bonilha, Ioulia Chatzistamou","doi":"10.2196/102908","DOIUrl":"https://doi.org/10.2196/102908","url":null,"abstract":"<p><strong>Background: </strong>Artificial intelligence (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.</p><p><strong>Objective: </strong>This crossover pilot study evaluated a curriculum-aligned custom GPT that was developed to support AI-scaffolded self-directed learning (SDL), preparation that remains student-initiated but is supported by structured Socratic prompting, compared with fully unguided traditional preparation in second-year medical students at the University of South Carolina, Floyd School of Medicine (USC Floyd SoM).</p><p><strong>Methods: </strong>Forty-four second-year medical students voluntarily participated in the two faculty-led pathology small group discussions (Cushing's disease and prostate cancer), using the custom GPT for one case and conventional resources for the other. Faculty assessed SDL documentation forms and small group discussion performance using standardized rubrics.</p><p><strong>Results: </strong>Students using the curriculum-aligned custom GPT achieved significantly higher SDL rubric scores (10.3 vs. 9.4 out of 12; mean difference 0.9, 95% CI 0.5-1.3; P<.001; Cohen d=0.71) and small group discussion scores (14.9 vs. 13.7 out of 16; mean difference 1.2, 95% CI 0.7-1.7; P<.001; Cohen d=0.78), with moderate to large effect sizes. Delayed retention (end-of-block examination items) did not differ significantly (Wilcoxon signed-rank P=.052). Students also reported significantly greater gains in understanding and pathophysiological reasoning. 34 out of 44 (77%) reported improved reasoning clarity, and 31 out of 44 (70%) improved conceptual understanding. Assessment of five prespecified AI-related concern domains did not identify major concerns.</p><p><strong>Conclusions: </strong>In this crossover pilot study, use of curriculum-aligned custom GPT was associated with higher faculty-assessed SDL documentation and small group discussion performance. Delayed knowledge retention did not differ significantly in the primary analysis. These findings support further investigation of curriculum-aligned AI tools in larger, multisite studies.</p><p><strong>Clinicaltrial: </strong>This pilot study was not registered in a public trial registry. As a minimal-risk, exempt educational pilot study conducted within a single course block, formal registration was not pursued; the authors note this as a limitation and recommend prospective registration for future, larger-scale studies of this intervention.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":" ","pages":""},"PeriodicalIF":13.9,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149184892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simulation-Based Radiology Training for Medical Technical Assistant Trainees: Quasi-Experimental Study.","authors":"Matthias J Witti, Joséphine-Charlotte Coleman, Constanze Richters, Johanna Schönberger, Mary-Anne de Ruijter, Alina Stortz, Melanie Bauer, Raisa Kiriakidou, Matthias Stadler","doi":"10.2196/97489","DOIUrl":"https://doi.org/10.2196/97489","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Objective: </strong>This study evaluated a digital radiology simulation module for medical technical assistant trainees in Germany, examining learning gains, group differences in motivational variables, and the moderating role of utility value.</p><p><strong>Methods: </strong>Fifty German medical technical assistant trainees (including 24 specializing in radiology and 26 focused on laboratory medicine) completed a pretest and posttest requiring them to arrange 12 steps of a contrast injection procedure in the correct order. All participants then engaged in an interactive computer simulation of the procedure. After the simulation, trainees rated their self-efficacy, as well as the intrinsic value and perceived utility value of the simulation. Learning gains were analyzed using a mixed-design ANOVA on the Spearman footrule distance composite score, and group differences in motivational variables were tested using independent-sample 2-tailed <i>t</i> tests. The moderating role of utility value on learning gains was examined using a mixed-design analysis of covariance.</p><p><strong>Results: </strong>Trainees showed significant improvement in procedural sequencing from pretest to posttest (<i>F</i>_1,48=22.05; <i>P</i><.001; η²=0.103), indicating effective learning across both groups. Radiology-specialized trainees reported higher utility value for the training than laboratory-focused trainees (<i>t</i>₄₆=4.43; <i>P</i><.001; Cohen <i>d</i>=1.28), whereas no group differences emerged in intrinsic value or self-efficacy. Contrary to our hypothesis, perceived utility value did not significantly moderate learning gains (<i>F</i>_1,46=0.92; <i>P</i>=.34).</p><p><strong>Conclusions: </strong>The simulation produced robust learning gains regardless of learner specialization or perceived relevance. While utility value perception differed markedly between groups, it did not translate into differential learning outcomes, suggesting that well-designed simulations can be effective across learners with varying levels of task relevance.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"12 ","pages":"e97489"},"PeriodicalIF":13.9,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13660458/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149184769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of the Bridge-in, Objective, Preassessment, Participatory Learning, Postassessment, and Summary-Based Instructional Model in Disaster Nursing: Quasi-Experimental Study.","authors":"Li Pei, Taoyu Jia, Yaxin Liu, Xiao-Li Pang, Muzi Li, Haoying Dou","doi":"10.2196/77728","DOIUrl":"https://doi.org/10.2196/77728","url":null,"abstract":"<p><strong>Background: </strong>The undergraduate stage is critical for developing nursing students' disaster nursing competencies; however, traditional lecture-based teaching often fails to promote active learning, and innovative instructional models remain underused in this field. The BOPPPS (Bridge-in, Objective, Preassessment, Participatory Learning, Postassessment, and Summary) model is a student-centered, closed-loop instructional framework that emphasizes active participatory learning and real-time feedback.</p><p><strong>Objective: </strong>This study aimed to evaluate the effects of a BOPPPS-based blended teaching model on disaster nursing competencies and academic outcomes among undergraduate nursing students.</p><p><strong>Methods: </strong>This study used a quasi-experimental design with 77 undergraduate nursing students allocated to 3 groups: an experimental group receiving BOPPPS-based blended teaching (n=27), a control group with traditional lecture-based teaching (n=25), and a comparison group without systematic instruction (n=25). Questionnaires were used to assess students' self-perceived disaster nursing knowledge, professional operational skills, and overall comprehensive competencies. The final examination assessed students' mastery of theoretical knowledge and applied reasoning.</p><p><strong>Results: </strong>All 3 groups showed significant within-group improvements in disaster competence (all <i>P</i><.001, n=77). Intergroup comparisons revealed significant differences (<i>H</i>=8.843, <i>P</i>=.01, η²=0.093); post hoc analysis showed that the experimental group (mean rank=48.79) scored significantly higher than the comparison group (mean rank=29.76, <i>P</i>_adj=.009), but not significantly higher than the control group (mean rank=38.32, <i>P</i>_adj=.34). The experimental group had a significantly higher overall course score than the control group (86.93, SD 5.08 vs 81.60, SD 6.90; <i>t</i>₅₀<i>=</i>-3.186, <i>P</i>=.002).</p><p><strong>Conclusions: </strong>The implementation of a disaster nursing curriculum positively impacts students' disaster nursing competency. Since the BOPPPS-based blended teaching model promotes active student engagement and improves teaching quality, we recommend its integration into disaster nursing education.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"12 ","pages":"e77728"},"PeriodicalIF":13.9,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13637988/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149115777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dependability of Entrustable Professional Activity Portfolios in Otorhinolaryngology Residency: Nationwide Generalizability and Decision Study.","authors":"Jeng-Wen Chen, Ching-Lin Hsieh, Chun-Hsiang Chang, Ya-Hui Wang, Wei-Chung Hsu, Pa-Chun Wang, Li-Jen Liao, Chun-Hou Liao, Mingchih Chen, Okki Dhona Laksmita","doi":"10.2196/104628","DOIUrl":"10.2196/104628","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Competency-based medical education increasingly uses digital workplace assessment platforms to collect longitudinal evidence of trainees' readiness for progressive responsibility. Entrustable professional activities (EPAs) translate competencies into observable clinical work, but individual workplace observations are context-dependent and may not support dependable progression decisions. Empirical guidance remains limited on how many observations, faculty raters, EPA titles, and workplace settings are needed for a dependable EPA portfolio.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to estimate EPA portfolio dependability for competency-based progression review using nationwide data from a digital workplace assessment platform in otorhinolaryngology-head and neck surgery residency training.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted a retrospective, nationwide cross-sectional generalizability and decision study using routinely collected EPA-based workplace assessment data from Taiwan's E-MyWay platform. The study included completed EPA observations from accredited otorhinolaryngology-head and neck surgery residency programs between August 2022 and May 2026. Faculty-assigned entrustment-supervision ratings were scored on a 5-level national EPA scale. The resident was the object of measurement, whereas faculty rater, EPA title, and workplace setting were measurement facets. The generalizability study partitioned rating variance into resident, facet, interaction, and residual components, which were subsequently used in decision-study projections of the generalizability coefficient, Phi coefficient, and absolute standard error of measurement under alternative portfolio configurations.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The analytic sample included 45,526 EPA observations involving 466 residents and 448 faculty raters across 35 training programs, 11 EPA titles, and 5 workplace settings. Residents had a median of 87 (IQR 47-124) observations and were assessed by a median of 9 (IQR 6-12) faculty raters across 11 (IQR 10-11) EPA titles and 5 (IQR 4-5) workplace settings. Single EPA observations were insufficient for resident-level summative interpretation. In the primary generalizability study, residual/encounter-level variance was the largest component, accounting for 40.1% of total variance; resident variance accounted for 26.9%, faculty rater variance accounted for 10.8%, and resident × faculty rater interaction for 10.4%. A study-defined well-distributed 10-observation portfolio achieved a generalizability coefficient of 0.77 but an absolute dependability coefficient (Phi) of 0.65. A 20-observation portfolio across 5 faculty raters, 5 EPA titles, and 3 workplace settings yielded a Phi of 0.76, and a 30-observation portfolio across 6 faculty raters, 6 EPA titles, and 4 workplace settings yielded a Phi of 0.80.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Nationwide digital workplace assessment data showed that EPA portfoli","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"12 ","pages":"e104628"},"PeriodicalIF":13.9,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13637980/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149115787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generative Language Models in Medical Education: From Advent to Entrustment.","authors":"Shiv Patil, Mert Karabacak, Sotirios Bisdas, Konstantinos Margetis","doi":"10.2196/105347","DOIUrl":"10.2196/105347","url":null,"abstract":"<p><p>Early discussions of generative language models in medical education emphasized their promise for simulation, digital patients, individualized feedback, learner assessment, health information dissemination, research support, and translation, while also warning about bias, privacy, academic integrity, misinformation, legal ambiguity, and unequal access. Since that first wave, generative AI has moved from novelty to routine exposure for learners, educators, researchers, and institutions. Medical education therefore needs a more mature framework than a catalog of opportunities and risks. This viewpoint argues that the next phase should be organized around educational entrustment: determining which functions can be delegated to AI systems, under what conditions, with what human supervision, and with what evidence of benefit. Building on recent proposals to apply entrustment to AI in health professions education, we operationalize the concept into a graduated, function-level model that specifies which educational functions may be delegated; at what stakes; and with what oversight, assessment, and governance. We classify use cases by educational stakes and AI autonomy and outline implications for assessment redesign, curriculum development, faculty capability, cognitive autonomy, equity, and institutional governance. The central challenge is whether medical schools can integrate these tools in ways that preserve clinical reasoning, professional identity, accountability, and fairness. The next generation of research should move beyond model performance on examinations and evaluate how AI changes learning, judgment, behavior, and patient care.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"12 ","pages":"e105347"},"PeriodicalIF":13.9,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13669701/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149115762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-Mediated Assessment of Continuing Medical Education: The Case-based Learning Intelligence Credit System (CLICS) Framework.","authors":"Somkiat Wattanasirichaigoon","doi":"10.2196/99520","DOIUrl":"https://doi.org/10.2196/99520","url":null,"abstract":"<p><strong>Unlabelled: </strong>Continuing medical education (CME) and continuing professional development (CPD) systems have traditionally relied on time-based credit allocation, using participation duration as a proxy for professional learning. Although administratively simple and scalable, this model does not reliably demonstrate whether physicians have engaged in meaningful learning, improved clinical reasoning, or critically appraised evidence. The emergence of generative AI creates an opportunity to rethink how physician learning is documented, assessed, and credited. This viewpoint proposes the Case-based Learning Intelligence Credit System (CLICS), a conceptual framework for translating AI-mediated clinical learning interactions into auditable evidence of reasoning-related engagement that could support CME/CPD credit. CLICS introduces the professional learning episode (PLE) as the basic unit of creditable learning: a coherent AI-mediated interaction demonstrating a clinically meaningful problem, reasoning development through iterative inquiry, contextual or evidentiary integration, and reflective synthesis. PLEs are evaluated using the proposed Practice Intelligence Score-7 (PIS-7) rubric, subject to human calibration and oversight; the rubric assesses observable reasoning behavior within the episode rather than the AI's answer, and qualifying PLEs may be translated into CME/CPD credit through threshold-based, human-auditable conversion rules. CLICS is not intended to replace traditional CME but to extend it as an optional, evidence-generating pathway for personalized, practice-embedded professional development. Its implementation requires iterative validation, stratified human audit, privacy-by-design architecture, antigaming controls, bias monitoring, and professional oversight. If validated, CLICS may enable identification of domain-specific areas for improvement and support personalized, adaptive learning pathways.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"12 ","pages":"e99520"},"PeriodicalIF":13.9,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13626552/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149115812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence as a Core Public Health Competency: Proposal and Perspectives in Education and Research.","authors":"Laura Brunelli, Federico Fonda, Silvio Brusaferro","doi":"10.2196/92246","DOIUrl":"10.2196/92246","url":null,"abstract":"<p><strong>Unlabelled: </strong>The continuous development of AI presents unprecedented opportunities for public health. This development prompts educators and researchers to consider how AI can be applied to and integrated into a rapidly evolving global landscape in which AI is reshaping health systems. In turn, this generates an urgent need for structured proposals that bridge theory and practice. In this viewpoint, we present perspectives and proposals on AI applications in public health. We operationalize AI literacy as a core public health competency, defined through knowledge, skills, and attitudes and scaled across professional roles. For education, we describe how AI can support learners, teachers, and organizations through personalized learning experiences, dynamic scenarios, immersive simulations, content preparation, and communication strategies. For research, we analyze how AI can be embedded as a transversal enabler supporting the World Health Organization's essential public health functions. We emphasize that AI should enhance, rather than replace, human capabilities and propose that AI literacy should be recognized as a core public health competency. We suggest that harnessing AI for public health education and research is, therefore, less a technological challenge than a collective responsibility shared by educators, researchers, and institutions.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"12 ","pages":"e92246"},"PeriodicalIF":13.9,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13625617/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149082057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}