JMIR Medical Education最新文献

Artificial intelligence (AI) is rapidly reshaping medical education, offering new opportunities to personalize learning, enhance research, and streamline administration. The aim of this study is to provide 12 practical, evidence-informed tips by drawing on current literature and real-world examples to guide the integration of AI into medical education, supporting educators across teaching, research, administration, and ethical domains. Key strategies include using adaptive learning platforms to tailor educational content, using AI tools to provide timely feedback, and incorporating AI-generated clinical scenarios in case-based learning. The importance of fostering AI literacy among students is emphasized, as well as utilizing AI-powered tools for efficient literature reviews, data analysis, and manuscript preparation. Administrative applications such as automating routine tasks, supporting strategic planning through data analysis, and enhancing faculty development with AI-driven platforms are also discussed. Ethical considerations are highlighted, with a focus on ensuring transparency, fairness, and accountability in all AI applications. By following these 12 tips, medical educators can leverage the benefits of AI to improve educational outcomes, increase efficiency, and prepare future clinicians for a technology-driven health care environment.

Background: Clinicians are central to treating tobacco use disorder, yet practical training is inconsistent, and confidence varies. Brief, text message-based microlearning may offer a low-burden way to strengthen foundational competencies in busy clinical settings.

Objective: This paper aims to evaluate whether a short SMS microlearning series improves clinicians' self-reported confidence in managing tobacco use disorder.

Methods: We conducted a single-arm, pre-post educational pilot at an academic medical center. A brief formative survey (13 items; 106 respondents) identified local knowledge gaps and informed message topics and sequencing. The 13-day series delivered 1 concise message per day with key teaching points and links to curated resources. The prespecified primary outcome was self-reported confidence in managing tobacco use disorder (1-100 scale) measured immediately before and after the series. Of the 34 clinicians who signed up, 22 completed the baseline questionnaire and enrolled (attendings: n=4, 18%; trainees: n=18, 82%). Changes in confidence among participants with paired ratings were tested with a paired t test. Engagement with embedded links was recorded.

Results: All enrolled participants completed the 13-day series; none unsubscribed. Postintervention confidence ratings were provided by 18 participants. Mean confidence increased from 60 (SD 16) at baseline to 85 (SD 10) after the series (t17=-10.71; P<.001). Embedded links were opened in 67% (178/266) of messages. Free-text feedback was predominantly positive and emphasized the convenience, clarity, and point-of-care usefulness of brief messages.

Conclusions: A brief SMS microlearning series was associated with a substantial improvement in clinicians' confidence to manage tobacco use disorder, with high completion and evidence of engagement. This low-cost, scalable approach appears practical for busy clinicians. Findings should be interpreted cautiously given the single-arm design, self-selection, and reliance on self-reported confidence rather than objective knowledge or clinical outcomes. Future studies should include a validated knowledge assessment, a randomized comparison, broader sampling, and follow-up to assess durability and impact on care.

Background: Medical education continues to favor didactic lectures as the predominant method of instruction. However, in recent years, there has been a shift toward active learning methodologies such as gamification.

Objective: This study aimed to describe the implementation of 3 open-access, web-based pharmacology games tailored for medical students: Cross DRUGs, Find the DRUG, and DRUGs Escape Room. The study also evaluated the impact of gamification on knowledge retention, student engagement, and learning experience in pharmacology education.

Methods: We used a quasi-experimental design to examine the effects of gamification on knowledge retention by comparing pretest and posttest scores between the gamer and control groups. Each week, students self-selected into either the gamer group or the control group based on personal preference. All students were provided with online access to the same lecture slides. Students in the control group completed both the pretest and posttest but did not play any of the games. A survey was administered to assess students' perceptions of gamification as a learning tool.

Results: Of the 72 students enrolled in the course, 49 (68%) agreed to participate, with 40 (56%) students completing both the pretest and posttest and being included in our analysis. As participation could vary weekly, an individual student might have appeared in both groups across different weeks, resulting in 59 gamer sessions and 20 control sessions. The mean pretest scores were 6.05 (SD 2.31) for the control group and 6.20 (SD 2.13) for the gamer group. The mean posttest scores were 6.90 (SD 2.02) for the control group and 8.47 (SD 1.30) for the gamer group. The gamer group exhibited significantly improved posttest scores (P=.006), while the control group did not (P=.21). Most respondents (25/30, 83%) found the games enjoyable and agreed that the games effectively helped them understand pharmacological concepts (24/30, 80%). Additionally, 70% (21/30) of students believed they learned better from the gaming format than from didactic lectures. Most favored a blended approach that combines lectures with games or case studies.

Conclusions: Gamification can serve as an effective complementary teaching tool for helping medical students learn pharmacological concepts.

Unlabelled: Artificial intelligence (AI) has the potential to transform medical training through adaptive learning, immersive simulations, automated assessments, and data-driven insights, offering solutions to persistent issues such as high student-to-faculty ratios, overcrowded classrooms, and limited clinical exposure. Globally, many universities have already embedded AI literacy and competencies into undergraduate, postgraduate, and continuing education programs, while in Vietnam, the use of AI in medical education remains limited and fragmented. Most students have little formal exposure to AI, and empirical evidence on faculty or institutional readiness is scarce. Experiences from other countries, including Malaysia, Palestine, and Oman, demonstrate that incremental adoption and faculty development can facilitate cultural acceptance and curricular innovation, providing useful lessons for Vietnam. At the same time, significant barriers remain. These include inadequate infrastructure in provincial universities, low levels of AI literacy among both students and educators, underdeveloped regulatory and ethical frameworks, and resistance to pedagogical change. Cost-effectiveness and sustainability are additional concerns in a middle-income context, where upfront investments must be balanced against long-term benefits and equitable access. Advancing AI in Vietnamese medical education will therefore require a coordinated national strategy that prioritizes infrastructure, AI literacy, faculty development, quality assurance, and sustainable funding models, alongside ethical and legal safeguards. By addressing these key foundations, Vietnam can harness AI not only to modernize medical education but also to strengthen preparedness for a digitally enabled health workforce.