ArticleJournal of medical Internet research2024
Ethical Considerations and Fundamental Principles of Large Language Models in Medical Education: Viewpoint.
Article in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- Large language model use in dental education: a cross-sectional multi-country study.Medical education online · 2026Article
- Performance Gaps and Optimization Strategies in Chinese Medical Large Language Models Based on MedBench: Evaluation Study.JMIR AI · 2026Article
- Performance of large language models in mitral valve surgery patient education: a comparative analysis.BMC medical informatics and decision making · 2026Article
- Authoritative Textbook-Augmented Large Language Models for High-Altitude Public Health Medical Education in the Xizang Autonomous Region: Cross-Sectional Comparative Evaluation Study.Journal of medical Internet research · 2026Article
- Article
- Large language model use in oral and maxillofacial surgery training: a national resident survey.Oral and maxillofacial surgery · 2026Article
- How accurately do large Language models interpret sport safeguarding principles: an evaluation using the International Olympic Committee framework.BMC sports science, medicine & rehabilitation · 2026Article
- Artificial intelligence as a surrogate brain: bridging neural dynamical models and data.National science review · 2026Review
- What the performance of multimodal LLMs on a national licensing exam teaches us about occupational therapy education.BMC medical education · 2026Article
- Hotspot Evolution and Future Prospects of Large Language Models in Medical Education: A Bibliometric Analysis.Advances in medical education and practice · 2026Article
- Integrating Artificial Intelligence into Medical Education in LMICs: A Narrative Review.Advances in medical education and practice · 2026Review
- A primer on artificial intelligence for palliative care educators.Palliative care and social practice · 2026Review
- Holographic Imaging Combined with VR Simulation in Obstetrics and Gynecology Resident Training: A Review on Skill Development and Patient Privacy Protection.International journal of women's health · 2026Review
- Large language models in emergency medicine education: opportunities, challenges, and implementation pathways.Frontiers in public health · 2026Review
- From algorithms to operating room: can large language models master China's attending anesthesiology exam? A cross-sectional evaluation.International journal of surgery (London, England) · 2026Article
- Role reconstruction among double-qualified nursing educators in the generative AI era: a qualitative study.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
- Comparison of the information of generative artificial intelligence large language models and professional guidelines regarding nutritional advice for orthodontic patients.BMC oral health · 2025Article
- Improving Large Language Model Applications in the Medical and Nursing Domains With Retrieval-Augmented Generation: Scoping Review.Journal of medical Internet research · 2025Article
- Application of DeepSeek-assisted problem-based learning in hematology residency training.BMC medical education · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This viewpoint article first explores the ethical challenges associated with the future application of large language models (LLMs) in the context of medical education. These challenges include not only ethical concerns related to the development of LLMs, such as artificial intelligence (AI) hallucinations, information bias, privacy and data risks, and deficiencies in terms of transparency and interpretability but also issues concerning the application of LLMs, including deficiencies in emotional intelligence, educational inequities, problems with academic integrity, and questions of responsibility and copyright ownership. This paper then analyzes existing AI-related legal and ethical frameworks and highlights their limitations with regard to the application of LLMs in the context of medical education. To ensure that LLMs are integrated in a responsible and safe manner, the authors recommend the development of a unified ethical framework that is specifically tailored for LLMs in this field. This framework should be based on 8 fundamental principles: quality control and supervision mechanisms; privacy and data protection; transparency and interpretability; fairness and equal treatment; academic integrity and moral norms; accountability and traceability; protection and respect for intellectual property; and the promotion of educational research and innovation. The authors further discuss specific measures that can be taken to implement these principles, thereby laying a solid foundation for the development of a comprehensive and actionable ethical framework. Such a unified ethical framework based on these 8 fundamental principles can provide clear guidance and support for the application of LLMs in the context of medical education. This approach can help establish a balance between technological advancement and ethical safeguards, thereby ensuring that medical education can progress without compromising the principles of fairness, justice, or patient safety and establishing a more equitable, safer, and more efficient environment for medical education.
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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.