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PreprintCited by 418Artificial Intelligence in Healthcare and EducationAcademic Writing and PublishingArtificial Intelligence in Education

OpenAI ChatGPT Generated Literature Review: Digital Twin in Healthcare

Open access
Aydın, Ömer, Karaarslan, Enis·2022·SSRN Electronic Journal
DOI 10.2139/ssrn.4308687
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Abstract

Literature review articles are essential to summarise the related work in the selected field. However, covering all related studies takes too much time and effort. This study questions how Artificial Intelligence can be used in this process. We used ChatGPT to create a literature review article to show the stage of the OpenAI ChatGPT artificial intelligence application. As the subject, the applications of Digital Twin in the health field were chosen. Abstracts of the last three years (2020, 2021 and 2022) papers were obtained from the keyword "Digital twin in healthcare" search results on Google Scholar and paraphrased by ChatGPT. Later on, we asked ChatGPT questions. The results are promising; however, the paraphrased parts had significant matches when checked with the Ithenticate tool. This article is the first attempt to show the compilation and expression of knowledge will be accelerated with the help of artificial intelligence. We are still at the beginning of such advances. The future academic publishing process will require less human effort, which in turn will allow academics to focus on their studies. In future studies, we will monitor citations to this study to evaluate the academic validity of the content produced by the ChatGPT.

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Crossref · 10.2139/ssrn.4308687

Cites

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Claims the paper makes

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Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • ChatGPT was listed in several sources as an efficient and promising tool for conducting comprehensive literature reviews and generating computer codes, thereby saving time for research steps requiring more human intelligence.

    “Specifically, ChatGPT was listed in several sources as an efficient and promising tool for conducting comprehensive literature reviews and generating computer codes, thereby saving time for the research steps that require more efforts from human intelligence (e.g., the focus on experimental design) [14,27,28,41,44,46,47,60,71,72].”

    From ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns (2023) · cites this paper as [71]

  • Generation of non-original, over-detailed, or excessive content can be an additional burden for researchers who should carefully supervise ChatGPT-generated content.

    “Third, the generation of non-original, over-detailed, or excessive content can be an additional burden for researchers who should carefully supervise the ChatGPT-generated content [14,24,25,26,65,71].”

    From ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns (2023) · cites this paper as [71]

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Authors
Aydın, Ö., & Karaarslan, E.
Affiliations
  • Manisa Celal Bayar University (TR)
  • Muğla University (TR)
Corresponding author
Karaarslan, Enis
ORCID
Venue
SSRN Electronic Journal
Publisher
Elsevier BV
Published
2022
ISSN
1556-5068 (ISSN-L)
DOI
10.2139/ssrn.4308687
OpenAlex
W4313262066
Cited by
391
References
0
Citations by year
2026: 19 · 2025: 67 · 2024: 130 · 2023: 170 · 2022: 4 · 2021: 1
Type
posted content
Language
en
Open access
Locations
  • SSRN Electronic Journal (acceptedVersion)
  • Muğla Sıtkı Koçman University Institutional Repository (submittedVersion, other-oa)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Social Sciences › Arts and Humanities › History and Philosophy of Science › Academic Writing and Publishing
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
Keywords
ChatGPT, large language models, digital twin, health care, AI-assisted academic writing, paraphrase, plagiarism detection, scholarly publishing

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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