ArticleBMC medical education2024
Assessing clinical medicine students' acceptance of large language model: based on technology acceptance model.
Article in BMC medical education, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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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.
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.
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Who cites it
15 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
- Large language model use in dental education: a cross-sectional multi-country study.Medical education online · 2026Article
- Artificial intelligence readiness in Pakistan's medical and dental education: training-phase decline, a knowledge-practice paradox, and the role of digital determinants of health.Medical education online · 2026Article
- Feasibility and Acceptability of A Self-Directed Virtual Interview Preparation Tool for Medical Students.Medical science educator · 2026Article
- Sufficient and necessary conditions for ChatGPT adoption in medical education: a combined partial least square-structural equation modelling and necessary condition analysis.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
- A scoping review of the use of generative artificial intelligence tools in health profession education.BMC medical education · 2026Article
- Physicians' acceptance of large language model-based clinical decision support tools in gynecologic oncology: a technology acceptance model study.Frontiers in digital health · 2026Article
- Awareness, attitudes, and utilization of large language models among healthcare students in Saudi Arabia: a cross-sectional analysis.Frontiers in medicine · 2026Article
- Feasibility of a Specialized Large Language Model for Postgraduate Medical Examination Preparation: Single-Center Proof-Of-Concept Study.JMIR formative research · 2025Article
- AI chatbots as 'pocket doctors': intimate health support for young women in Lebanon.BMC public health · 2025Article
- Development of a Clinical Clerkship Mentor Using Generative AI and Evaluation of Its Effectiveness in a Medical Student Trial Compared to Student Mentors: 2-Part Comparative Study.JMIR medical education · 2025Article
- Mindfulness and AI adoption: extending the technology acceptance model for Chinese media students.Frontiers in psychology · 2025Article
- A comparative analysis of perceptions and evaluations of Simodont® Dental Trainer: a decade of virtual simulation.Frontiers in oral health · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
While large language models (LLMs) have demonstrated significant potential in medical education, there is limited understanding of medical students' acceptance of LLMs and the factors influencing their use. This study explores medical students' acceptance of LLMs in learning and examines the factors influencing this acceptance through the lens of the Technology Acceptance Model (TAM). A questionnaire survey conducted among Chinese medical students revealed a high willingness to use LLMs in their studies. The findings suggest that attitudes play a crucial role in predicting medical students' behavioral intentions to use LLMs, mediating the effects of perceived usefulness, perceived ease of use, and perceived risk. Additionally, perceived risk and social influence directly impact behavioral intentions. This study provides compelling evidence supporting the applicability of the TAM to the acceptance of LLMs in medical education, highlighting the necessity for medical students to utilize LLMs as an auxiliary tool in their learning process.
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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.