ArticleJournal of medical education and curricular development
Knowledge, Readiness, and Perception of Medical Students Toward Medical Artificial Intelligence: A Cross-Sectional Study.
Article in Journal of medical education and curricular development. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 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
1 citing paper 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
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
Background: Given recent advances in artificial intelligence (AI) in medical education and healthcare, it is essential to examine the perceptions and readiness of medical students. As future medical professionals, their ability to utilize this emerging technology effectively is crucial. Therefore, the present study aimed to examine medical AI knowledge, readiness, and perceptions among medical students in medical education and healthcare, and the risks and disadvantages associated with it in Iran. Methods: This cross-sectional study was conducted among Iranian medical students in 2025. The questionnaire used in this study consisted of three parts: the first part, socio-demographic characteristics; the second part, basic knowledge and students' perceptions of medical education, healthcare, and risks and disadvantages of medical AI; and the third part, students' readiness for medical AI. The data were analyzed using SPSS 22 and Excel 2019 software. Results: Of the total 280 medical students participating in the present study, 55.4% were female, and 60% were in the preclinical phase. The results showed that respondents demonstrated greater AI readiness in the dimensions of vision and ethics and possessed a high level of knowledge regarding the terms "artificial intelligence," "neural networks," and "deep learning." More than 70% of respondents reported a high perception of medical AI in its three dimensions. A significant relationship exists between the medical AI readiness score and gender, working family/close friends, knowledge of AI, and three dimensions of students' perceptions of medical AI. Conclusion: Enhancing students' knowledge, readiness, and understanding of medical AI can equip professionals with improved medical and decision-making skills. These professionals can make more informed decisions and reduce medical errors with AI tools.
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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.