ArticleBMC medical education2023
AI in medical education: medical student perception, curriculum recommendations and design suggestions.
Article in BMC medical education, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
58 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Effectiveness of AI-assisted medical education for Chinese undergraduate medical students: a meta-analysis.BMC medical education · 2025Pooled it
- Real-time AI feedback for novice intraoral imaging - a pilot three-arm randomized study.BMC medical education · 2026Trial
- AI In Leukemia Diagnostics: Complementing the Pathologist's Role.International journal of laboratory hematology · 2026Review
- Performance of ChatGPT, Claude, and AMBOSS on the European Board of Urology In-Service Assessment and Alignment With the European Association of Urology 2025 Guidelines: Comparative Study.JMIR formative research · 2026Article
- Integrating Generative AI Into Clinical Reasoning Education for Medical Students: Mixed Methods Study.JMIR medical education · 2026Article
- Widely available, general-purpose generative AI applications for radiology education: determining the teaching quality of synthetic pediatric neuroradiology images.Pediatric radiology · 2026Article
- Awareness, Educational Needs, and Curriculum Preferences Regarding AI and Medical Big Data Education Among Clinical Medicine Undergraduates: Cross-Sectional Survey Study.JMIR formative research · 2026Article
- Generative AI's Impact on the Mental Health of Medical Students: Scenario Analysis.JMIR medical education · 2026Article
- Experiences and Perceptions of Clinical and Graduate Medical Students Regarding AI in Syria: Cross-Sectional Study.JMIR medical education · 2026Article
- Effectiveness and students' perception of tutor-guided AI navigation in undergraduate medical education in Sri Lanka: a quasi-experimental study.BMC medical education · 2026Article
- Factors influencing large language model adoption among dental students: a cross-sectional study.Scientific reports · 2026Article
- Adoption of generative AI chatbots among medical postgraduates at two universities in China: patterns, attitudes, and concerns.BMC medical education · 2026Article
- Article
- Assessing the level of readiness for digital transformation in medicine: students' perspectives on the use of artificial intelligence in health : a cross-sectional study.BMC medical education · 2026Article
- Construction and implementation of an AI-enhanced progressive training model for clinical competency development in obstetrics and gynecology residents.BMC medical education · 2026Article
- Artificial intelligence-assisted training for rib fracture interpretation: a prospective study in undergraduate medical students.World journal of emergency surgery : WJES · 2026Observational
- Generative Artificial Intelligence in Medical Training: Utilization Patterns Across Knowledge, Patient Care, Systems Reasoning, and Innovation.Medical science educator · 2026Article
- Framework for AI integration into the undergraduate medical curricula: a scoping review.BMC medical education · 2026Article
- Factors associated with the experience of AI tools for creating health education materials: cross-sectional study using an extended UTAUT model.BMC medical education · 2026Article
- The post-pandemic transformation in Pathophysiology teaching strategies.Frontiers in medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Medical AI has transformed modern medicine and created a new environment for future doctors. However, medical education has failed to keep pace with these advances, and it is essential to provide systematic education on medical AI to current medical undergraduate and postgraduate students. To address this issue, our study utilized the Unified Theory of Acceptance and Use of Technology model to identify key factors that influence the acceptance and intention to use medical AI. We collected data from 1,243 undergraduate and postgraduate students from 13 universities and 33 hospitals, and 54.3% reported prior experience using medical AI. Our findings indicated that medical postgraduate students have a higher level of awareness in using medical AI than undergraduate students. The intention to use medical AI is positively associated with factors such as performance expectancy, habit, hedonic motivation, and trust. Therefore, future medical education should prioritize promoting students' performance in training, and courses should be designed to be both easy to learn and engaging, ensuring that students are equipped with the necessary skills to succeed in their future medical careers.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.