ArticleJMIR human factors2026
Utilization of AI Among Medical Students and Development of AI Education Platforms in Medical Institutions: Cross-Sectional Study.
Article in JMIR human factors, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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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.
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Who cites it
9 citing papers in PubMed.
- Piloting an AI Introduction Program for Incoming Medical Students: A Novel Approach to Medical Education.Medical science educator · 2026Article
- Article
- Reconciling how clinical reasoning is learned in the age of artificial intelligence.NPJ digital medicine · 2026Article
- Mixed-methods study on GenAI Usage, dependence behaviors, and standardized application paths among Chinese medical students.NPJ digital medicine · 2026Article
- From traditional classroom to AI-enhanced flipped classroom: a three-year pedagogical evolution for international students in pharmacology.BMC medical education · 2026Article
- Generative AI and knowledge management in health education: a cross-sectional study of students' digital literacy and attitudes from a developing-country context.BMC medical education · 2026Article
- Article
- Bridging medical education and public health: a pedagogical model to develop future physicians' capacity in audience-tailored health communication.Frontiers in public health · 2026Article
- Personality, usage, and perceptions of AI in medical education: evidence from senior pre-clinical students in China.Frontiers in psychology · 2026Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The emergence of artificial intelligence (AI) is driving digital transformation and reshaping medical education in China. Numerous medical schools and institutions are actively implementing AI tools for case-based learning, literature analysis, and lecture support. This expanding application is accelerating the adoption of localized AI platforms, which are poised to become integral components in the coming years. Objective: The primary aim of this study was to investigate the current use of AI tools among medical students, including usage frequency, commonly used platforms, and purposes of use. The second aim was to explore students' needs and expectations toward AI-powered medical education platforms by collecting and assessing student feedback, and to identify practical requirements across disciplines and academic stages to inform more effective platform design. Methods: Based on the task-technology fit model and 5 hypotheses, an anonymous online questionnaire was conducted to assess AI usage in learning, gather student feedback on AI-powered medical education platforms, and evaluate expected functionalities. The survey was conducted from March 1 to May 31, 2025, using a convenience sampling method to recruit medical students from various disciplines across Shanghai, China. The sample size was determined at 422, accounting for a 10% rate of invalid responses. The questionnaire was developed and distributed online via Wenjuanxing and promoted through WeChat groups and in-person interviews. Data analysis was conducted employing IBM SPSS Statistics (v 27.0). Results: A total of 428 valid questionnaires were collected. The average frequency of AI-assisted learning among medical students was 5.06 (SD 2.05) times per week. Over 90% (388/428) of the students used more than 2 AI tools in their daily tasks. Students from different disciplines, educational stages, and academic systems demonstrated different usage patterns and expectations for AI-powered medical education platforms. Conclusions: AI technology is widely accepted by medical students and is extensively applied across various aspects of medical education. Significant differences are observed in usage patterns across disciplines, educational stages, and academic systems. Understanding the actual needs of students is crucial for the construction of AI-powered medical education platforms.
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Registered trials
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