Evidence map›Paper›PMID 38650670›Full record

ArticleJournal of multidisciplinary healthcare2024

The Application of ChatGPT in Medicine: A Scoping Review and Bibliometric Analysis.

Jie Wu, Yingzhuo Ma, Jun Wang, Mingzhao Xiao

Abstract readScoping Review
In one paragraph

Article in Journal of multidisciplinary healthcare, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 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

4 authors.

Jie WuDepartment of Nursing, the First Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China.
Yingzhuo MaDepartment of Nursing, the First Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China.
Jun WangDepartment of Nursing, the First Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China.
Mingzhao XiaoDepartment of Urology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: ChatGPT has a wide range of applications in the medical field. Therefore, this review aims to define the key issues and provide a comprehensive view of the literature based on the application of ChatGPT in medicine. Methods: This scope follows Arksey and O'Malley's five-stage framework. A comprehensive literature search of publications (30 November 2022 to 16 August 2023) was conducted. Six databases were searched and relevant references were systematically catalogued. Attention was focused on the general characteristics of the articles, their fields of application, and the advantages and disadvantages of using ChatGPT. Descriptive statistics and narrative synthesis methods were used for data analysis. Results: Of the 3426 studies, 247 met the criteria for inclusion in this review. The majority of articles (31.17%) were from the United States. Editorials (43.32%) ranked first, followed by experimental studys (11.74%). The potential applications of ChatGPT in medicine are varied, with the largest number of studies (45.75%) exploring clinical practice, including assisting with clinical decision support and providing disease information and medical advice. This was followed by medical education (27.13%) and scientific research (16.19%). Particularly noteworthy in the discipline statistics were radiology, surgery and dentistry at the top of the list. However, ChatGPT in medicine also faces issues of data privacy, inaccuracy and plagiarism. Conclusion: The application of ChatGPT in medicine focuses on different disciplines and general application scenarios. ChatGPT has a paradoxical nature: it offers significant advantages, but at the same time raises great concerns about its application in healthcare settings. Therefore, it is imperative to develop theoretical frameworks that not only address its widespread use in healthcare but also facilitate a comprehensive assessment. In addition, these frameworks should contribute to the development of strict and effective guidelines and regulatory measures.

Indexed as

bibliometric analysisChatGPTlarge language modelmedicinenursingscoping review

Identifiers

PMID38650670
PMCPMC11034560

What OpenQuestion holds

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LicenceCC BY-NC
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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.