Using ChatGPT in Medical Research: Current Status and Future Directions
Open accessAbstract
Objective: This review aims to evaluate the current evidence on the use of the Generative Pre-trained Transformer (ChatGPT) in medical research, including but not limited to treatment, diagnosis, or medication provision. Methods: This review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched Google Scholar, Web of Science, PubMed, and Medline to identify studies published between 2022 and 2023 that aimed to utilize ChatGPT in medical research. All identified references were stored in EndNote. Results: We initially identified 114 articles, out of which six studies met the inclusion and exclusion criteria for full-text screening. Among the six studies, two focused on drug development (33.33%), two on literature review writing (33.33%), and one each on medical report improvement, provision of medical information, improving research conduct, data analysis, and personalized medicine (16.67% each). Conclusion: ChatGPT has the potential to revolutionize medical research in various ways. However, its accuracy, originality, academic integrity, and ethical issues must be thoroughly discussed and improved before its widespread implementation in clinical research and medical practice.
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A systematic literature review was conducted using the PRISMA methodology.
“As part of this investigation, a systematic literature review was conducted using the PRISMA methodology (Supplementary Materials) [40,45,46,47,48,49,50].”
The integration of ChatGPT into medical practices and healthcare settings presents challenges and ethical considerations.
“The integration of ChatGPT into medical practices and healthcare settings presents challenges and ethical considerations.”
Ethical concerns arise when content generated by AI is mistaken for the expertise of a human.
“Ethical concerns arise when content generated by AI is mistaken for the expertise of a human.”
One challenge is that ChatGPT's responses are based on the data on which it was trained, which may result in biased or inaccurate information.
“One challenge is that the responses of ChatGPT are based on the data on which it was trained, which may result in biased or inaccurate information.”
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- Authors
- Ruksakulpiwat, S., Kumar, A., & Ajibade, A.
- Affiliations
- Mahidol University (TH)
- Case Western Reserve University (US)
- Corresponding author
- Ruksakulpiwat, Suebsarn
- Venue
- Journal of Multidisciplinary Healthcare
- Publisher
- Informa UK Limited
- Published
- 2023-05
- Volume
- Volume 16
- Pages
- 1513-1520
- ISSN
- 1178-2390 (ISSN-L)
- PMC
- PMC10239248
- OpenAlex
- W4379376212
- Cited by
- 156
- References
- 17
- Citations by year
- 2026: 10 · 2025: 36 · 2024: 79 · 2023: 31
- FWCI
- 6.23
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Journal of Multidisciplinary Healthcare (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Dove Medical Press (Taylor and Francis Group) (submittedVersion, cc-by-nc)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- ChatGPT, large language models, medical research, drug development, literature review writing, personalized medicine, research ethics, academic integrity, artificial intelligence in healthcare
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
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