Evidence map›Paper›PMID 38971715›Full record

ArticleJournal of medical Internet research2024

Ethical Considerations and Fundamental Principles of Large Language Models in Medical Education: Viewpoint.

Li Zhui, Li Fenghe, Wang Xuehu, Fu Qining, Ren Wei

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

36 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Li ZhuiDepartment of Vascular Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.ORCID 0000-0003-3467-7063
Li FengheDepartment of Vascular Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.ORCID 0000-0003-3965-8152
Wang XuehuDepartment of Vascular Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.ORCID 0000-0003-4918-5088
Fu QiningDepartment of Vascular Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.ORCID 0000-0002-5205-2908
Ren WeiDepartment of Vascular Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.ORCID 0009-0002-1280-878X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Artificial IntelligenceEducation, MedicalHumansLanguagePrivacyacademic integrityAIartificial intelligencedata protectiondata securityeducational researchethicsintellectual property rightslarge language modelsLLMsmedical educationmedical ethicsprivacy and data risks

Identifiers

PMID38971715
PMCPMC11327620

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.