ArticleCureus2023
Assessing the Utilization of Large Language Models in Medical Education: Insights From Undergraduate Medical Students.
Article in Cureus, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses 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.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines 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
- Impact of generative AI in medical education in India: a systematic review.Frontiers in artificial intelligence · 2025Pooled it
- Feasibility of a Randomized Controlled Trial of Large AI-Based Linguistic Models for Clinical Reasoning Training of Physical Therapy Students: Pilot Randomized Parallel-Group Study.JMIR formative research · 2025Trial
- AI in UK Medical Education: A Framework for Curriculum Reform.JMIR medical education · 2026Article
- Assessment of Stage Two Hypertension Treatment Plans Written by Generative AI.Journal of clinical medicine · 2026Article
- The application of large language models in orthopedic postgraduate education: potentials, challenges, and future prospects.Journal of orthopaedic surgery and research · 2026Review
- Article
- A scoping review of the use of generative artificial intelligence tools in health profession education.BMC medical education · 2026Article
- Awareness, attitudes, and utilization of large language models among healthcare students in Saudi Arabia: a cross-sectional analysis.Frontiers in medicine · 2026Article
- OSCEai: personalized interactive learning for undergraduate medical education.Canadian medical education journal · 2025Article
- Artificial intelligence in undergraduate medical education: an updated scoping review.BMC medical education · 2025Article
- ChatGPT and Other Large Language Models in Medical Education - Scoping Literature Review.Medical science educator · 2025Review
- Widespread use of ChatGPT and other Artificial Intelligence tools among medical students in Uganda: A cross-sectional study.PloS one · 2025Article
- Exploring Radiology Postgraduate Students' Engagement with Large Language Models for Educational Purposes: A Study of Knowledge, Attitudes, and Practices.The Indian journal of radiology & imaging · 2025Article
- Shaping the future of medical education: A cross-sectional study on ChatGPT attitude and usage among medical students in Sudan.PloS one · 2025Article
- Article
- Impact of Large Language Models on Medical Education and Teaching Adaptations.JMIR medical informatics · 2024Article
- Article
- Learning Tools Using ChatGPT in the Biochemistry Class: Creating Notes and Performance on Exams.Biochemistry and molecular biology education : a bimonthly publication of the International Union of Biochemistry and Molecular BiologyArticle
- Exploring Medical Students' Perceptions Regarding ChatGPT and AI Studying at the University of Niš: A Study on Usage, Attitudes, and Linguistic Influence-Single-Centered Study in Serbia-A Paradoxical Ally?Journal of medical education and curricular developmentArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background Artificial intelligence (AI) has the potential to be integrated into medical education. Among AI-based technology, large language models (LLMs) such as ChatGPT, Google Bard, Microsoft Bing, and Perplexity have emerged as powerful tools with capabilities in natural language processing. With this background, this study investigates the knowledge, attitude, and practice of undergraduate medical students regarding the utilization of LLMs in medical education in a medical college in Jharkhand, India. Methods A cross-sectional online survey was sent to 370 undergraduate medical students on Google Forms. The questionnaire comprised the following three domains: knowledge, attitude, and practice, each containing six questions. Cronbach's alphas for knowledge, attitude, and practice domains were 0.703, 0.707, and 0.809, respectively. Intraclass correlation coefficients for knowledge, attitude, and practice domains were 0.82, 0.87, and 0.78, respectively. The average scores in the three domains were compared using ANOVA. Results A total of 172 students participated in the study (response rate: 46.49%). The majority of the students (45.93%) rarely used the LLMs for their teaching-learning purposes (chi-square (3) = 41.44, p < 0.0001). The overall score of knowledge (3.21±0.55), attitude (3.47±0.54), and practice (3.26±0.61) were statistically significantly different (ANOVA F (2, 513) = 10.2, p < 0.0001), with the highest score in attitude and lowest in knowledge. Conclusion While there is a generally positive attitude toward the incorporation of LLMs in medical education, concerns about overreliance and potential inaccuracies are evident. LLMs offer the potential to enhance learning resources and provide accessible education, but their integration requires further planning. Further studies are required to explore the long-term impact of LLMs in diverse educational contexts.
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