{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,18]],"date-time":"2026-10-18T01:19:09Z","timestamp":1792286349219,"version":"4.4.0"},"reference-count":62,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Med Educ"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n Artificial intelligence (AI) systems are becoming increasingly relevant in everyday clinical practice, with Food and Drug Administration\u2013approved AI solutions now available in many specialties. This development has far-reaching implications for doctors and the future medical profession, highlighting the need for both practicing physicians and medical students to acquire the knowledge, skills, and attitudes necessary to effectively use and evaluate these technologies. Currently, however, there is limited experience with AI-focused curricular training and continuing education.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This paper first introduces a novel blended learning curriculum including one module on AI for medical students in Germany. Second, this paper presents findings from a qualitative postcourse evaluation of students\u2019 knowledge and attitudes toward AI and their overall perception of the course.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n Clinical-year medical students can attend a 5-day elective course called \u201cMedicine in the Digital Age,\u201d which includes one dedicated AI module alongside 4 others on digital doctor-patient communication; digital health applications and smart devices; telemedicine; and virtual\/augmented reality and robotics. After course completion, participants were interviewed in semistructured small group interviews. The interview guide was developed deductively from existing evidence and research questions compiled by our group. A subset of interview questions focused on students\u2019 knowledge, skills, and attitudes regarding medical AI, and their overall course assessment. Responses were analyzed using Mayring\u2019s qualitative content analysis. This paper reports on the subset of students\u2019 statements about their perception and attitudes toward AI and the elective\u2019s general evaluation.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n We conducted a total of 18 group interviews, in which all 35 (100%) participants (female=11, male=24) from 3 consecutive course runs participated. This produced a total of 214 statements on AI, which were assigned to the 3 main categories \u201cAreas of Application,\u201d \u201cFuture Work,\u201d and \u201cCritical Reflection.\u201d The findings indicate that students have a nuanced and differentiated understanding of AI. Additionally, 610 statements concerned the elective\u2019s overall assessment, demonstrating great learning benefits and high levels of acceptance of the teaching concept. All 35 students would recommend the elective to peers.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n The evaluation demonstrated that the AI module effectively generates competences regarding AI technology, fosters a critical perspective, and prepares medical students to engage with the technology in a differentiated manner. The curriculum is feasible, beneficial, and highly accepted among students, suggesting it could serve as a teaching model for other medical institutions. Given the growing number and impact of medical AI applications, there is a pressing need for more AI-focused curricula and further research on their educational impact.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/65220","type":"journal-article","created":{"date-parts":[[2025,5,26]],"date-time":"2025-05-26T16:48:19Z","timestamp":1748278099000},"page":"e65220-e65220","source":"Crossref","is-referenced-by-count":14,"title":["Novel Blended Learning on Artificial Intelligence for Medical Students: Qualitative Interview Study"],"prefix":"10.2196","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5473-2141","authenticated-orcid":false,"given":"Zoe S","family":"Oftring","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2642-8625","authenticated-orcid":false,"given":"Kim","family":"Deutsch","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8597-5189","authenticated-orcid":false,"given":"Daniel","family":"Tolks","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1561-5174","authenticated-orcid":false,"given":"Florian","family":"Jungmann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8031-2973","authenticated-orcid":false,"given":"Sebastian","family":"Kuhn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2025,5,26]]},"reference":[{"issue":"14","key":"R1","first-page":"633","volume":"115","author":"Kuhn","journal-title":"Deutsches \u00c4rzteblatt"},{"issue":"1","key":"R2","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","article-title":"High-performance medicine: the convergence of human and artificial intelligence","volume":"25","author":"Topol","journal-title":"Nat Med"},{"issue":"1","key":"R3","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-020-00324-0","article-title":"The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database","volume":"3","author":"Benjamens","journal-title":"NPJ Digit Med"},{"key":"R4","unstructured":"Artificial intelligence and machine learning (AI\/ML)-enabled medical devices. 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