JMIR Medical Education最新文献

{"title":"Methodological Concerns in AI Medical Education Frameworks.","authors":"Syeda Urooj Fatima, Emaan Fatima, Ragni Kumari","doi":"10.2196/101696","DOIUrl":"10.2196/101696","url":null,"abstract":"","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"12 ","pages":"e101696"},"PeriodicalIF":13.9,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13335939/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148399619","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":"Authors' Reply: Methodological Concerns in AI Medical Education Frameworks.","authors":"Juan S Izquierdo-Condoy, Marlon Arias-Intriago, Laura Montero Corrales, Esteban Ortiz-Prado","doi":"10.2196/104447","DOIUrl":"10.2196/104447","url":null,"abstract":"","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"12 ","pages":"e104447"},"PeriodicalIF":13.9,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13335937/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148399574","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":"Voice Cloning Using AI vs Traditional Audio Recording for Prerecorded Courses in Medical Pedagogy: Randomized Controlled Trial.","authors":"Antoine Gavoille, Fabien Subtil, Mikaïl Nourredine","doi":"10.2196/86569","DOIUrl":"10.2196/86569","url":null,"abstract":"<p><strong>Background: </strong>Prerecorded courses are increasingly used in medical education, and audio quality is known to influence learners' comprehension and engagement. Traditional audio recording, however, is time-consuming and may be uncomfortable for some educators. Advances in generative artificial intelligence (AI) now allow for realistic voice cloning, but its pedagogical value compared with conventional recording has not been assessed.</p><p><strong>Objective: </strong>This study aimed to evaluate the usefulness and perception of AI-based voice cloning for prerecorded courses in medical pedagogy compared with traditional audio recording.</p><p><strong>Methods: </strong>We conducted a randomized trial among fourth- and fifth-year medical students at a French university. Participants accessed four 10-minute prerecorded lectures on critical appraisal of medical research. The control group received lectures with audio recorded by the teacher, whereas the intervention group received audio generated from the teacher's cloned voice using a commercial AI text-to-speech system, with identical slides and scripts. The primary outcome was the total score on 2 online tests (17 multiple-choice questions on knowledge acquisition and 15 multiple-choice questions on knowledge application). Secondary outcomes included satisfaction ratings, course viewing metrics, and production time.</p><p><strong>Results: </strong>A total of 88 students were randomized, and 64 (72.7%) watched at least 15 seconds of video and, thus, were included in the modified intention-to-treat population. Mean total test scores did not differ significantly between the AI voice cloning and audio recording groups (51.2, SD 4.9 vs 51.8, SD 6.9 out of 100; adjusted mean difference -0.9, 95% CI -2.7 to 4.4; P=.60). Satisfaction was high in both groups. Production time was shorter with AI (22.5, SD 6.45 vs 35, SD 7.07 minutes per video).</p><p><strong>Conclusions: </strong>We did not detect a significant difference in learning outcomes or satisfaction between AI voice cloning and conventional recording, whereas AI voice cloning reduced production time, making it a practical alternative for prerecorded medical courses. Nevertheless, some students may perceive synthetic voices as less authentic, representing a potential barrier to widespread adoption.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"12 ","pages":"e86569"},"PeriodicalIF":13.9,"publicationDate":"2026-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13376843/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148370140","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 for Assessment and Feedback in Medical Education: Bibliometric Mapping Study and Thematic Evidence Map.","authors":"Zihang Zhao, Zihan Liu, Liang Guo, Teng Pan, Yousheng Zhang, Chenxiang Miao, Yiting Ge, Yipeng Wang, Xin Hu, Xin Wang, Ruipeng Zhang, Zhiyong Hou","doi":"10.2196/98949","DOIUrl":"10.2196/98949","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Artificial intelligence (AI), particularly generative AI and large language models, is increasingly used for assessment-related tasks in medical education. Existing overviews often address AI in medical education broadly, limiting assessment-specific interpretation of functions, settings, learner stages, and responsible-AI reporting domains.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to map the literature on AI for assessment and feedback in medical education, including publication trends, bibliometric structure, assessment functions, AI types, settings, learner stages, and reporting of validity, reliability, fairness, integrity, transparency, human oversight, implementation, and governance.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted a bibliometric mapping study incorporating structured thematic evidence-map coding. Web of Science Core Collection, Scopus, and PubMed were searched from January 1, 2015, to April 8, 2026. Document selection and main evidence-map coding were based primarily on titles, abstracts, and bibliographic metadata, with targeted ambiguity resolution. Because reporting domains may appear mainly in full-text sections, all 435 included records underwent full-text sensitivity analysis for the 8 reporting domains. Coding reliability was assessed by two coders using percent agreement and Cohen κ before adjudication. Exploratory subgroup analyses and an excluding-2026 partial-year sensitivity analysis were conducted.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Searches identified 14,968 records; 435 were included after deduplication and selection. Overall, 399 (91.7%) records were indexed in the post-ChatGPT period. Generative AI was coded in 310 (71.3%) records, and large language models in 301 (69.2%) records. In the assessment-function umbrella analysis, learner performance evaluation accounted for 270 (62.1%) records, feedback for 93 (21.4%) records, assessment content generation for 65 (14.9%) records, and other or unclear functions for 7 (1.6%) records. The most common settings were board-style examinations (n=151, 34.7%) and written examinations (n=88, 20.2%); undergraduate medical education was the most represented learner stage (n=172, 39.5%). Full-text-confirmed reporting was most frequent for reliability (n=288, 66.2%) and implementation (n=231, 53.1%); intermediate for validity (n=158, 36.3%), fairness (n=132, 30.3%), and transparency (n=130, 29.9%); and less frequent for governance (n=57, 13.1%), human oversight (n=46, 10.6%), and integrity (n=26, 6%). Stage 1 κ values ranged from 0.785 to 0.895, and stage 2 κ values ranged from 0.809 to 0.880. Excluding 65 partial-year 2026 records did not change the overall interpretation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The indexed English-language literature on AI for assessment and feedback in medical education expanded rapidly in the post-ChatGPT period and was concentrated in generative AI, large language models, examination-oriented assessment, a","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"12 ","pages":"e98949"},"PeriodicalIF":13.9,"publicationDate":"2026-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13376851/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148376401","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":"Parenthood in US Medical Training Across Specialty Groups: Scoping Review.","authors":"Zuleica M Santiago Delgado, Elizabeth M Vaughan, Mary E Jones, Forrest Jones, Dylan Bridwell, Laura R Porterfield","doi":"10.2196/87284","DOIUrl":"10.2196/87284","url":null,"abstract":"<p><strong>Background: </strong>Residency and fellowship are demanding phases characterized by intense schedules, limited autonomy, sleep deprivation, and hierarchical environments. Training years often coincide with peak reproductive age, presenting trainees with the dilemma of delaying parenthood or managing training and parenthood concurrently.</p><p><strong>Objective: </strong>This scoping review examines recent literature on factors influencing the experiences of male and female physician trainee parents across various specialties.</p><p><strong>Methods: </strong>Four databases were searched for studies published from January 2014 through January 2025 examining pregnancy, parenthood, or lactation outcomes among physician trainees and distinguishing between specialties. Eligible studies were screened, data extracted using predefined criteria, and findings thematically coded. Chi-square and Fisher exact tests assessed specialty-related differences for each theme.</p><p><strong>Results: </strong>The authors identified 15,861 records, removed 6440 duplicates, and excluded 8989 records. Further, 432 full-text papers were reviewed, with 105 papers included in the final analysis: surgical (n=59), medical subspecialty (n=30), primary care (n=11), and multiple specialties (n=5). Surgical and medical subspecialty studies more often identified interpersonal and policy challenges than primary care. Surgical studies highlighted issues such as pregnancy-related bias, lack of support, rigid schedules, and training-parenthood conflict. Primary care studies more often identified conflicts between parenthood and patient care responsibilities (primary care: 3/11, 27% vs surgical: 3/59, 5% and medical subspecialty studies 0/30, 0%, P=.01). Negative well-being impacts from training and parenthood were reported more often in primary care (3/11, 27%) and surgical studies (13/59, 22%) than in medical subspecialties (1/30, 3%, P=.03).</p><p><strong>Conclusions: </strong>Trainees face physician parenthood challenges across social ecological levels, with surgical and subspecialty trainees experiencing more systemic barriers and primary care trainees more patient care-related concerns.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"12 ","pages":"e87284"},"PeriodicalIF":13.9,"publicationDate":"2026-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13325462/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148370097","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":"Exploring Ambient Artificial Intelligence to Enhance Learning and Feedback During Operating Room-to-Intensive Care Unit Handoffs: Co-Design and Simulation Study.","authors":"Laleh Jalilian, Federico Lorenzo Barra, Tristan Grogan, Jason Lee, Achuta Kadambi","doi":"10.2196/85666","DOIUrl":"10.2196/85666","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Operating room (OR)-to-intensive care unit (ICU) handoffs are among the most complex and high-risk communication events in perioperative care. Despite the implementation of structured checklists, trainees often receive limited feedback on their communication skills, and simulation-based education rarely provides objective data on communication performance and checklist adherence. This study explores how an ambient artificial intelligence (AI) handoff assistant used during simulation-based training of OR-to-ICU handoff discussions can enhance clinical communication training and AI literacy by mapping spoken handoff discussions to handoff checklist items, providing immediate feedback on checklist item omissions, and generating a structured handoff note that functions as a feedback-rich learning artifact.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to co-design and evaluate an ambient AI handoff assistant that transcribes spoken OR-to-ICU handoff communication, maps the discussion to handoff checklist items, generates a structured handoff note for educational review, and provides immediate feedback on handoff completeness during simulated OR-to-ICU handoff discussions in a low-fidelity educational setting.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A 2-phase mixed-methods study was conducted within the University of California, Los Angeles, Department of Anesthesiology and Perioperative Care (July-October 2025). Phase 1 comprised co-design interviews with 4 clinician educators to identify limitations of current handoff training and inform AI feature development. Phase 2 involved an error analysis, as well as evaluations of usability, workload, and educational impact, conducted through ten 60-minute simulation sessions with pairs of medical students and first-year residents. Quantitative measures included the Physician Task Load Index, System Usability Scale, and a postsimulation survey; qualitative data from co-design sessions and simulation debrief interviews were thematically analyzed.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Educators highlighted inconsistent checklist use and the absence of objective feedback on learners' communication skills as key areas that could benefit from structured documentation of handoff discussions using AI. Error analysis of the ambient AI handoff assistant revealed a mean of 3.6 (SD 1.2) errors per note, with incorrect output being the most frequent error type. There was no statistically significant difference between the ambient AI handoff assistant and the paper checklist with respect to the Physician Task Load Index and System Usability Scale measures. Trainees valued real-time transcripts and structured handoff notes for reflection of communication practices, and exposure to AI documentation errors enhanced critical thinking and awareness of AI technology limitations.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The ambient AI handoff assistant mapped simulated handoff discussions to checklist items and","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"12 ","pages":"e85666"},"PeriodicalIF":13.9,"publicationDate":"2026-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13326726/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148377096","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":"Digital Simulation-Based Ultrasound Training for Physiotherapy Students: Prospective Randomized Controlled Trial.","authors":"Samuel Fernández-Carnero, Belen Diaz-Pulido, Jorge Méndez-Rodriguez, Daniel Pecos-Martin, Santiago Garcia-Miguel, Alexander Achalandabaso-Ochoa, Nicolas Cuenca-Zaldívar, Fermín Naranjo-Cinto","doi":"10.2196/87897","DOIUrl":"10.2196/87897","url":null,"abstract":"<p><strong>Background: </strong>Ultrasound education has traditionally relied on on-site training, but scalable digital solutions are increasingly needed.</p><p><strong>Objective: </strong>This study aimed to evaluate the effectiveness of a simulation-based online ultrasound platform compared with traditional on-site training.</p><p><strong>Methods: </strong>A prospective randomized blinded study was conducted with 68 physiotherapy students (n=34 per group). Participants were assigned to a simulation-based online training platform (WAZO) or traditional on-site instruction. Both groups completed identical theoretical and practical assessments. Item response theory using a Rasch model was applied to evaluate item difficulty and student ability.</p><p><strong>Results: </strong>No significant differences were found between the online and on-site groups in theoretical scores (mean 4.94, SD 1.47 vs mean 5.08, SD 1.14; P=.65) or practical performance variables, including probe handling (26/34, 76.5% vs 28/34, 82.4%; P=.37) and structure identification (24/34, 70.6% vs 22/34, 64.7%; P=.19). Measurement outcomes also showed no significant differences, including structure diameter (mean 3.78, SD 0.79 mm vs mean 3.98, SD 1.27 mm; P=.46) and structure surface distance (mean 3.94, SD 1.97 mm vs mean 3.24, SD 0.64 mm; P=.06). Item response theory analysis identified image optimization, structure diameter, and structure surface distance as the most difficult items, while probe handling and structure identification were the most informative. The model demonstrated high discriminative performance (area under the curve=0.93), with sensitivity of 87% and specificity of 80%.</p><p><strong>Conclusions: </strong>Simulation-based online ultrasound training provides comparable theoretical and practical outcomes to traditional on-site instruction, supporting its use as a scalable and accessible educational alternative.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"12 ","pages":"e87897"},"PeriodicalIF":13.9,"publicationDate":"2026-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13327370/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148377135","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":"Exploring the Role of Early Career Medical Professionals From a Digital-Oriented University in Germany in Promoting Digital Health in Professional Settings: Qualitative Interview Study.","authors":"Christopher Koch, Jan P Ehlers, Julia Nitsche, Theresa Sophie Busse","doi":"10.2196/86107","DOIUrl":"10.2196/86107","url":null,"abstract":"<p><strong>Background: </strong>To address care delivery gaps, the health care system must embrace innovative digital solutions. Additionally, the rising integration of digitalization as a topic into medical education is providing students with broader opportunities to engage with digitalization overall. As digital health becomes an increasingly integral component of medical education and health care practice, digitally affine early career medical professionals constitute a vital resource for advancing digitalization within the health care sector.</p><p><strong>Objective: </strong>This study examines how early career medical professionals from Witten/Herdecke University in Germany, with various courses focusing on digitalization, acquire digital knowledge, apply it across diverse practical contexts-ranging from start-ups and corporate environments to traditional clinical settings-and how they contribute to the advancement of digitalization within the entire health care sector.</p><p><strong>Methods: </strong>Using a qualitative approach, 19 interviews were conducted with early career medical professionals who graduated in the last 15 years at Witten/Herdecke University. Subsequently, the interviews were transcribed and analyzed using a deductive-inductive approach.</p><p><strong>Results: </strong>The findings reveal that medical graduates often acquire digital skills through intensive self-study and learning on the job, integrating them in various ways into their professional lives. Moreover, while graduates recognize their high potential to make their own contribution to the advancement of digitalization, they also face significant barriers such as knowledge gaps, limited resources, and complex regulations, which hinder their ability to contribute to digitalization in a variety of professional settings. Medical graduates report that they face a pressing need for enhanced knowledge access, improved institutional frameworks, and supportive policy measures to maximize their potential in advancing digitalization initiatives.</p><p><strong>Conclusions: </strong>Recent medical graduates represent an underused resource for health care digitalization. Unlocking this potential requires coordinated action across medical education, health care institutions, and policymaking to create appropriate conditions for graduates to actively drive digitalization.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"12 ","pages":"e86107"},"PeriodicalIF":13.9,"publicationDate":"2026-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13293368/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148319466","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":"Effectiveness of Artificial Intelligence-Assisted Peer Teaching in Orthopedic Clinical Education: Historical Cohort Study.","authors":"Chengcheng Yu, Fangcai Li, Ning Zhang, Hejia Hu, Hanxu Huang, Jingkai Wang, Yiqing Tao, Yinan Wu","doi":"10.2196/87959","DOIUrl":"10.2196/87959","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Peer teaching is an established pedagogical approach in medical education; yet, traditional methods face challenges including inconsistent knowledge support, variable teaching quality, and limited scalability. Artificial intelligence (AI) large language models offer potential to augment peer teaching by providing on-demand access to medical knowledge and clinical reasoning support. However, AI integration within structured peer teaching has not been systematically evaluated in clinical education.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to evaluate the effectiveness of AI-assisted peer teaching compared to traditional peer teaching in orthopedic clinical education, with respect to knowledge acquisition, clinical skills development (particularly clinical reasoning), student engagement, and 3-month knowledge retention.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This historical cohort study compared 2 consecutive cohorts of medical students (aged 20-27 years, 108/190, 56.8% male) at the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China. All eligible students from each cohort were enrolled. The control group (2021 cohort, n=96, taught in 2024) received traditional peer teaching; the intervention group (2022 cohort, n=94, taught in 2025) received AI-assisted peer teaching with access to DeepSeek-V3. Primary outcomes were assessed using a validated 50-item multiple-choice examination (0-100 points) and a 4-station Objective Structured Clinical Examination (OSCE; 0-100 points) with standardized rubrics (intraclass correlation coefficient&gt;0.85). Secondary outcomes included student engagement and satisfaction (5-point Likert scales) and AI usage metrics. Assessments were conducted at baseline, postintervention (8 weeks), and 3-month follow-up. Analysis of covariance adjusted for baseline knowledge, prior AI experience, and learning interest to address observed baseline imbalances.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Using independent samples t tests (α=.05, 2-tailed), the AI-assisted group demonstrated significantly higher postintervention knowledge scores (mean 79.69, SD 8.41 vs mean 75.33, SD 9.26; mean difference=4.36, 95% CI 1.84-6.87; P&lt;.001; Cohen d=0.49). OSCE total scores were significantly higher (mean 80.95, SD 7.57 vs mean 76.24, SD 9.23; mean difference=4.71, 95% CI 2.31-7.11; P&lt;.001; d=0.56), with clinical reasoning showing the largest effect (mean difference=2.22, 95% CI 1.18-3.25; P&lt;.001; d=0.61). Analysis of covariance adjusted results remained significant for all primary outcomes (adjusted knowledge difference=3.52, P=.002; adjusted OSCE difference=4.52, P&lt;.001). At 3-month follow-up (174/190, 91.6%), the AI-assisted group maintained higher knowledge scores (mean 77.36, SD 8.60 vs mean 72.84, SD 10.42; mean difference=4.52, 95% CI 1.68-7.36; P=.002; d=0.47), with similar knowledge decay rates between groups.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This study provides the first system","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"12 ","pages":"e87959"},"PeriodicalIF":13.9,"publicationDate":"2026-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13347076/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148319493","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":"Impact of a Practical, Hands-On, Continuing Professional Development Course About AI in Health Care Professions Education on the Perceptions and Behaviors of Health Care Educators: Qualitative Case Study.","authors":"Justin G Peacock, Jerusalem Merkebu, Anita Samuel","doi":"10.2196/87381","DOIUrl":"10.2196/87381","url":null,"abstract":"<p><strong>Background: </strong>Artificial intelligence (AI) is increasingly being used in many aspects of society, including health care and education. AI has the potential to enhance health care delivery, education, and administration. Health care trainees will be required to master these AI technologies. To teach trainees to effectively and ethically leverage AI technologies, educators must be appropriately trained and empowered to use these technologies.</p><p><strong>Objective: </strong>We developed a health professions education course to enable health care professionals to overcome their fears and concerns about integrating multimodal generative AI technologies in daily practice. The course was also designed to foster generative AI skills and confidence in educational, administrative, research, and clinical activities.</p><p><strong>Methods: </strong>Using a multimethod approach, we analyzed data gathered from three different sources using the 6-phase reflexive thematic analysis by Braun and Clarke. This involved familiarization with the data sources, generating initial codes, developing, refining, and defining the themes, and finally, writing up the results.</p><p><strong>Results: </strong>Our findings indicate that 16 of 21 (76%) learners initially described apprehension or fear toward AI technologies. After experiential engagement with AI technologies together with their peers, they were able to shift their perspectives and gain confidence to integrate AI tools in their daily practice.</p><p><strong>Conclusions: </strong>A brief 6-week continuing professional development course on the use of AI technologies for health care professional educators, focused on experiential and peer-based learning, resulted in shifts in perception, affect, and behavior toward AI technologies. It also propelled learners to shift increasingly outward in their discussion, application, and preliminary advocacy for AI technologies in their daily practice.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"12 ","pages":"e87381"},"PeriodicalIF":13.9,"publicationDate":"2026-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13290435/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148309814","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}