ArticleJMIR medical informatics2024
Impact of Large Language Models on Medical Education and Teaching Adaptations.
Article in JMIR medical informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The performance of ChatGPT and other large language models on multiple-choice questions in biomedical disciplines: A meta-analysis.Anatomical sciences education · 2026Pooled it
- LLM-Based Psychiatric Interview Simulation: Technical Development and Pilot Expert-Based Content Validation of a Voice Prototype.Academic psychiatry : the journal of the American Association of Directors of Psychiatric Residency Training and the Association for Academic Psychiatry · 2026Article
- Large Language Models in German Continuing Medical Education Assessments: Protocol for a Fully Crossed Experimental Study.JMIR research protocols · 2026Article
- Large Language Model-Generated Differential Diagnoses in Radiology Education: Comparison with a Standard Casebook.Diagnostics (Basel, Switzerland) · 2026Article
- Acceptance and use of GenAI among medical and health sciences students in Saudi Arabia: an extended TAM study.BMC medical education · 2026Article
- A comparative benchmark of DeepSeek-R1 on the USMLE: surpassing human and AI performance averages.Clinics (Sao Paulo, Brazil) · 2026Article
- Large language models versus human examinee performance on Israeli anesthesiology board examinations.Scientific reports · 2026Article
- The effect of artificial intelligence-supported applications on critical thinking motivation and clinical decision making in nursing students: path analysis.BMC medical education · 2026Article
- AI-Driven Objective Structured Clinical Examination Generation in Digital Health Education: Comparative Analysis of Three GPT-4o Configurations.JMIR medical education · 2026Article
- Application effect and teaching evaluation of case-based learning combined with ChatGPT in ophthalmology clinical teaching.Frontiers in medicine · 2026Article
- A primer on artificial intelligence for palliative care educators.Palliative care and social practice · 2026Review
- Hotspot Evolution and Future Prospects of Large Language Models in Medical Education: A Bibliometric Analysis.Advances in medical education and practice · 2026Article
- AI-Enhanced Social Robotic Versus Computer-Based Virtual Patients for Clinical Reasoning Training in Medical Education: Observational Crossover Cohort Study.Journal of medical Internet research · 2025Observational
- A bi-linguistic comparative analysis of ChatGPT-4, Gemini, and Claude performance on Polish medical-dental final examinations.Scientific reports · 2025Article
- Comparative performance of neurosurgery-specific, peer-reviewed versus general AI chatbots in bilingual board examinations: evaluating accuracy, consistency, and error minimization strategies.Acta neurochirurgica · 2025Article
- The Role of Large Language Models (LLMs) in Hepato-Pancreato-Biliary Surgery: Opportunities and Challenges.Cureus · 2025Article
- Large Language Models' Responses to Spinal Cord Injury: A Comparative Study of Performance.Journal of medical systems · 2025Article
- The ethical challenges in the integration of artificial intelligence and large language models in medical education: A scoping review.PloS one · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Unlabelled: This viewpoint article explores the transformative role of large language models (LLMs) in the field of medical education, highlighting their potential to enhance teaching quality, promote personalized learning paths, strengthen clinical skills training, optimize teaching assessment processes, boost the efficiency of medical research, and support continuing medical education. However, the use of LLMs entails certain challenges, such as questions regarding the accuracy of information, the risk of overreliance on technology, a lack of emotional recognition capabilities, and concerns related to ethics, privacy, and data security. This article emphasizes that to maximize the potential of LLMs and overcome these challenges, educators must exhibit leadership in medical education, adjust their teaching strategies flexibly, cultivate students' critical thinking, and emphasize the importance of practical experience, thus ensuring that students can use LLMs correctly and effectively. By adopting such a comprehensive and balanced approach, educators can train health care professionals who are proficient in the use of advanced technologies and who exhibit solid professional ethics and practical skills, thus laying a strong foundation for these professionals to overcome future challenges in the health care sector.
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.