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Using ChatGPT in Medical Research: Current Status and Future Directions
Suebsarn Ruksakulpiwat1, Ayanesh Kumar2, Anuoluwapo Ajibade3
1Department of Medical Nursing, Faculty of Nursing, Mahidol University, Bangkok, Thailand.
Generative Pre-trained Transformer (ChatGPT) shows promise in medical research for drug development and literature reviews. Further validation is needed to address accuracy, originality, and ethical concerns before clinical application.
Area of Science:
- Artificial Intelligence in Medicine
- Natural Language Processing in Healthcare
- Computational Biology
Background:
- The integration of advanced AI tools like Generative Pre-trained Transformer (ChatGPT) into medical research is rapidly evolving.
- Evaluating the current evidence base for ChatGPT's applications in diverse medical research domains is crucial.
Purpose of the Study:
- To systematically review and assess the existing literature on the utilization of ChatGPT in medical research.
- To identify key areas of application, including but not limited to treatment, diagnosis, and medication provision.
Main Methods:
- Adherence to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.
- Comprehensive literature search across major databases: Google Scholar, Web of Science, PubMed, and Medline.
- Inclusion of studies published between 2022 and 2023 focusing on ChatGPT in medical research.
Main Results:
- Initial identification of 114 articles, with six studies meeting the inclusion criteria.
- Key applications identified: drug development (33.33%), literature review writing (33.33%), medical report enhancement, medical information provision, research conduct improvement, data analysis, and personalized medicine (16.67% each).
Conclusions:
- ChatGPT demonstrates significant potential to transform various aspects of medical research.
- Critical challenges including accuracy, originality, academic integrity, and ethical considerations require thorough investigation and resolution.
- Widespread implementation in clinical research and practice necessitates further development and validation of AI tools.
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