ArticleBMC medical education2026
Awareness, attitudes, and educational use of artificial intelligence among medical students: a large cross-sectional survey.
Article in BMC medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Medical Students' Attitudes, Perceptions, and Self-Reported Familiarity With AI in Health Care: Systematic Review and Meta-Analysis.JMIR medical education · 2026Pooled it
- Artificial intelligence readiness and its association with artificial intelligence literacy among Chinese medical students: a latent profile analysis.Frontiers in public health · 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) is increasingly influencing medical education and clinical practice. Understanding medical students’ awareness, attitudes, and usage patterns of AI is essential for guiding curriculum development and ensuring responsible integration into undergraduate medical training.
methodsA nationwide cross-sectional survey was conducted between November and December 2025 among undergraduate medical students in Türkiye. A structured questionnaire assessed demographic characteristics, AI awareness and frequency of use, educational and clinical applications, attitudes toward AI, ethical concerns, and future educational expectations. A composite AI Attitude Score was calculated using three Likert-scale items, and internal consistency was evaluated using Cronbach’s alpha. Descriptive and inferential statistical analyses were performed.
resultsA total of 1,346 medical students were included. Overall, 81.1% reported awareness of AI applications in medicine. AI tools were predominantly used for educational purposes (73.3%), whereas clinical or simulation-based use was limited (11.6%). The mean composite AI Attitude Score was 3.41 ± 0.61, indicating moderately positive perceptions (Cronbach’s alpha = 0.71). Ethical concerns were reported by 67.4% of participants. A majority (79.1%) expressed interest in receiving formal AI-related education. No significant differences were observed between preclinical and clinical students in overall attitudes or educational demand; however, clinical students reported significantly greater use of AI in clinical contexts (p < 0.001).
conclusionMedical students demonstrate high awareness and generally positive attitudes toward AI; however, clinical integration remains limited and ethical concerns are prevalent. These findings underscore the need for structured, ethically grounded AI education within undergraduate medical curricula. Findings should be interpreted with consideration of potential self-selection bias due to voluntary online participitation.
Indexed as
Identifiers
What OpenQuestion holds
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.