Perspectives on the application of large language models in healthcare
https://doi.org/10.47093/2713-069X.2023.4.4.48-55
Abstract
Large language models have become a new significant breakthrough in the field of artificial intelligence. They are changing approaches to machine learning from models that solve narrow problems and require large amounts of data with the known answers for training, to generative models that are fine tunable to solve specific problems using a small number of examples with the known answers or even none at all. Medicine is one of the areas in which the use of large language models can become extremely useful. The review presents data on the latest achievements in the use of large language models for medical tasks, prospects for using these models as the basis for the digital assistants for doctors and patients, as well as existing regulatory and ethical barriers to the development of this breakthrough technology for addressing healthcare challenges.
About the Authors
A. E. Andreychenko
K-Skai LLC
Russian Federation
Russian Federation
Anna E. Andreychenko – Cand. of Sci. (Physics and Mathematics), Head of Artificial Intelligence
Varkaus Embankment, 17, Petrozavodsk, 185031
A. V. Gusev
Federal Research Institute for Health Organization and Informatics; Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies
Russian Federation
Russian Federation
Aleksandr V. Gusev – Cand. of Sci. (Technology), Senior Researcher, Department of Scientific Foundations of Healthcare Organization, Senior Researcher
Dobrolyubova str., 11, Moscow, 127254
Petrovka str., 24/1, Moscow, 127051
References
1. Yang R., Tan T.F., Lu W., et al. Large language models in health care: development, applications, and challenges. Health Care Science. 2023; 2(4): 255–263. https://doi.org/10.1002/hcs2.61
2. Thirunavukarasu A.J., Ting D.S.J., Elangovan K., et al. Large language models in medicine. Nat Med. 2023; 29: 1930–1940. https://doi.org/10.1038/s41591-023-02448-8
3. Meskó B., Topol E.J. The imperative for regulatory oversight of large language models (or generative AI) in healthcare. npj Digit. Med. 2023; 6(1): 120. https://doi.org/10.1038/s41746-023-00873-0
4. Language models and linguistic theories beyond words. Nat Mach Intell. 2023; 5: 677– 678. https://doi.org/10.1038/s42256-023-00703-8
5. Kung T.H., Cheatham M., Medenilla A., et al. Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models. PLOS Digital Health. 2023; 2(2): e0000198. https://doi.org/10.1371/journal.pdig.0000198
6. Kaneda Y., Takahashi R., Kaneda U., et al. Assessing the Performance of GPT-3.5 and GPT- 4 on the 2023 Japanese Nursing Examination. Cureus. 2023; 15(8): e42924. https://doi.org/doi:10.7759/cureus.42924
7. Roos J., Kasapovic A., Jansen T., Kaczmarczyk R. Artificial Intelligence in Medical Education: Comparative Analysis of ChatGPT, Bing, and Medical Students in Germany. JMIR Med Educ. 2023; 9: e46482. https://doi.org/10.2196/46482
8. Singhal K., Aziz S., Tu T., et al. Large language models encode clinical knowledge. Nature. 2023; 620: 172–180. https://doi.org/10.1038/s41586-023-06291-2
9. Sarraju A., Bruemmer D., Van Iterson E., et al. Appropriateness of Cardiovascular Disease Prevention Recommendations Obtained From a Popular Online Chat-Based Artificial Intelligence Model. JAMA. 2023; 329(10): 842–844. https://doi.org/10.1001/jama.2023.1044
10. Chen S., Kann B.H., Foote M.B., et al. Use of Artificial Intelligence Chatbots for Cancer Treatment Information. JAMA Oncol. Published online. 2023. https://doi.org/10.1001/jamaoncol.2023.2954
11. Birkun A., Gautam A. Large language model-based chatbot as a source of advice on first aid in heart attack. Current Problems in Cardiology. 2023: 1(49): 102048. https://doi.org/10.1016/j.cpcardiol.2023.102048
12. Jiang L.Y., Liu X.C., Nejatian N.P., et al. Health system-scale language models are allpurpose prediction engines. Nature. 2023; 619: 357–362. https://doi.org/10.1038/s41586-023-06160-y
13. Moskatel L.S., Niushen Z. The utility of ChatGPT in the assessment of literature on the prevention of migraine: an observational, qualitative study. Frontiers in Neurology, 2023, 14: 1225223. https://doi.org/10.3389/fneur.2023.1225223
14. Lower K., Seth I., Lim B., Seth N. ChatGPT-4: Transforming Medical Education and Addressing Clinical Exposure Challenges in the Post-pandemic Era. Indian J Orthop. 2023; 57: 1527–1544. https://doi.org/10.1007/s43465-023-00967-7
Review
For citations:
Andreychenko A.E., Gusev A.V. Perspectives on the application of large language models in healthcare. National Health Care (Russia). 2023;4(4):48-55. (In Russ.) https://doi.org/10.47093/2713-069X.2023.4.4.48-55
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