Evidence map›Paper›PMID 39196640›Full record

ArticleJournal of medical Internet research2024

Current Status of ChatGPT Use in Medical Education: Potentials, Challenges, and Strategies.

Tianhui Xu, Huiting Weng, Fang Liu, Li Yang, Yuanyuan Luo, Ziwei Ding, Qin Wang

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 43 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
43citing papers in PubMed, 3 pooled it
–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

43 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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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

7 authors.

Tianhui XuClinical Nursing Teaching and Research Section, The Second Xiangya Hospital of Central South University, Changsha, China.ORCID 0009-0007-9564-6347
Huiting WengClinical Nursing Teaching and Research Section, The Second Xiangya Hospital of Central South University, Changsha, China.ORCID 0000-0002-6071-4369
Fang LiuClinical Nursing Teaching and Research Section, The Second Xiangya Hospital of Central South University, Changsha, China.ORCID 0000-0002-1419-2803
Li YangClinical Nursing Teaching and Research Section, The Second Xiangya Hospital of Central South University, Changsha, China.ORCID 0000-0002-4308-5118
Yuanyuan LuoXiangya School of Nursing, Central South University, Changsha, China.ORCID 0009-0007-0889-5098
Ziwei DingXiangya School of Nursing, Central South University, Changsha, China.ORCID 0009-0004-4151-0266
Qin WangClinical Nursing Teaching and Research Section, The Second Xiangya Hospital of Central South University, Changsha, China.ORCID 0000-0003-1640-8337

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

ChatGPT, a generative pretrained transformer, has garnered global attention and sparked discussions since its introduction on November 30, 2022. However, it has generated controversy within the realms of medical education and scientific research. This paper examines the potential applications, limitations, and strategies for using ChatGPT. ChatGPT offers personalized learning support to medical students through its robust natural language generation capabilities, enabling it to furnish answers. Moreover, it has demonstrated significant use in simulating clinical scenarios, facilitating teaching and learning processes, and revitalizing medical education. Nonetheless, numerous challenges accompany these advancements. In the context of education, it is of paramount importance to prevent excessive reliance on ChatGPT and combat academic plagiarism. Likewise, in the field of medicine, it is vital to guarantee the timeliness, accuracy, and reliability of content generated by ChatGPT. Concurrently, ethical challenges and concerns regarding information security arise. In light of these challenges, this paper proposes targeted strategies for addressing them. First, the risk of overreliance on ChatGPT and academic plagiarism must be mitigated through ideological education, fostering comprehensive competencies, and implementing diverse evaluation criteria. The integration of contemporary pedagogical methodologies in conjunction with the use of ChatGPT serves to enhance the overall quality of medical education. To enhance the professionalism and reliability of the generated content, it is recommended to implement measures to optimize ChatGPT's training data professionally and enhance the transparency of the generation process. This ensures that the generated content is aligned with the most recent standards of medical practice. Moreover, the enhancement of value alignment and the establishment of pertinent legislation or codes of practice address ethical concerns, including those pertaining to algorithmic discrimination, the allocation of medical responsibility, privacy, and security. In conclusion, while ChatGPT presents significant potential in medical education, it also encounters various challenges. Through comprehensive research and the implementation of suitable strategies, it is anticipated that ChatGPT's positive impact on medical education will be harnessed, laying the groundwork for advancing the discipline and fostering the development of high-caliber medical professionals.

Indexed as

Education, MedicalHumansPlagiarismartificial intelligencechat generative pretrained transformerChatGPTclinical practicemedical educationnatural language processing

Identifiers

PMID39196640
PMCPMC11391159

What OpenQuestion holds

Textmetadata
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.