Open Access Short Communication

Chat Generative Pre-trained Transformer uses in the future regarding clinical and translational medicine

by Mical Michael 1,*
1
Orthopedics department, south Kenall, 555 SW 89 Street, Suite 503, Miami, Florida, 33196, USA
*
Author to whom correspondence should be addressed.
IJCMR  2024 2(2):23; https://doi.org/10.61466/ijcmr2020003
Received: 12 February 2024 / Accepted: 4 March 2024 / Published Online: 5 March 2024

Abstract

An artificial intelligence-driven Chatbot called Chat Generative Pre-trained Transformer, created by Open artificial intelligence, is making waves in many industries. Its foundation in the Generative Pre-trained Transformer language model is where its name originates. The most promising aspect of Chat Generative Pre-trained Transformer is that, compared to other artificial intelligence models, it can provide responses to text input that are almost human-like through the use of deep learning techniques. The public's increasing reliance on artificial intelligence technology is indicated by its quick integration across a range of businesses. Therefore, it is crucial to assess Chat Generative Pre-trained Transformer's possible effects on clinical and translational medicine research in academic settings seriously.


Copyright: © 2024 by Michael. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Share and Cite

ACS Style
Michael, M. Chat Generative Pre-trained Transformer uses in the future regarding clinical and translational medicine. International Journal of Clinical Medical Research, 2024, 2, 23. https://doi.org/10.61466/ijcmr2020003
AMA Style
Michael M. Chat Generative Pre-trained Transformer uses in the future regarding clinical and translational medicine. International Journal of Clinical Medical Research; 2024, 2(2):23. https://doi.org/10.61466/ijcmr2020003
Chicago/Turabian Style
Michael, Mical 2024. "Chat Generative Pre-trained Transformer uses in the future regarding clinical and translational medicine" International Journal of Clinical Medical Research 2, no.2:23. https://doi.org/10.61466/ijcmr2020003
APA style
Michael, M. (2024). Chat Generative Pre-trained Transformer uses in the future regarding clinical and translational medicine. International Journal of Clinical Medical Research, 2(2), 23. https://doi.org/10.61466/ijcmr2020003

Article Metrics

Article Access Statistics

References

  1. Stokel-Walker C. Chat Generative Pre-trained Transformer listed as author on research papers: many scientists disapprove. Nature. 2023;613(7945):620-621.
  2. Open artificial intelligence. Chat Generative Pre-trained Transformer general FAQ. 2023. Accessed February 10, 2023.
  3. Gao C, Howard F, Markov N, et al. Comparing scientific abstracts generated by Chat Generative Pre-trained Transformer to original abstracts using an artificial intelligence output detector, plagiarism detector, and blinded human reviewers. 2022.
  4. Communications of the ACM. ICML bans papers written by Chat Generative Pre-trained Transformer and artificial intelligence language tools. 2023. Accessed February 10, 2023.
  5. Thorp HH. Chat Generative Pre-trained Transformer is fun, but not an author. Science. 2023;379(6630):313.
  6. van Dis E, Bollen J, Zuidema W, van Rooij R, Bockting C. Chat-Generative Pre-trained Transformer: five priorities for research. Nature. 2023;614(7947):224-226.
  7. Stokel-Walker C, VanNoorden R.What Chat Generative Pre-trained Transformer and generative artificial intelligence mean for science. Nature. 2023;614(7947):214-216.
  8. The artificial intelligence writing on the wall. Nat Mach. 2023;5(1):1.
  9. Seyhan, A. Lost in translation: the valley of death across preclinical and clinical divide – identification of problems and overcoming obstacles. Transl Med Commun. 2019;4.
  10. Samuel G, Chubb J, Derrick G. Boundaries between research ethics and ethical research use in artificial intelligence health research. J Empir Res Hum. 2021;16(3):325-337.