Potential of artificial intelligence and search engines to successful passing the test part of primary accreditation and primary specialized accreditation
https://doi.org/10.15829/1728-8800-2024-4202
EDN: ZWOPRD
Abstract
ChatGPT is a language model that has many benefits and applications in healthcare and medicine. It can be useful for medical professionals in various fields, including research, diagnostics, patient monitoring, and education. However, the use of GPT chat also entails a number of ethical issues and limitations, such as accuracy, plagiarism, copyright infringement and bias.
The study aim was to assess the potential of passing primary accreditation and primary specialized accreditation using artificial intelligence — ChatGPT, and the Yandex search engine.
The study consisted of 2 stages. The study revealed that using ChatGPT made it possible to pass General Medicine primary accreditation and Cardiology primary specialized accreditation. Using ChatGPT4 significantly accelerated the test. However, despite the significant acceleration of artificial intelligence, the quality of the answers does not allow successful accreditation in all specialties. A tendency was noted that the subjects began to trust the bot's answers to a greater extent without checking it.
About the Authors
E. A. Zheleznova
Branch of Hadassah Medical LCC.
Russian Federation
Russian Federation
Skolkovo, Moscow
V. V. Vlasov
National Research University "Higher School of Economics"
Russian Federation
Russian Federation
Moscow
A. V. Vlasova
Sechenov First Moscow State Medical University
Russian Federation
Russian Federation
Moscow
T. I. Depui
Branch of Hadassah Medical LCC.
Russian Federation
Russian Federation
Skolkovo, Moscow
I. V. Podobed
Research Medical Center "Gerontology"
Russian Federation
Russian Federation
Moscow
References
1. Floridi L, Chiriatti M. GPT-3: its nature, scope, limits, and consequences. Minds Machines. 2020;30:681-94.
2. Dave T, Athaluri SA, Singh S. ChatGPT in medicine: an overview of its applications, advantages, limitations, future prospects, and ethical considerations. Front Artif Intell. 2023;6:1169595.
3. Biswas SS. Role of Chat GPT in public health. Ann Biomed Eng. 2023;51:868-9.
4. Heckerman DE, Horvitz E, Nathwani BN. Toward normative expert systems: Part I the Pathfinder project. Methods of Information in Medicine. 1992;31:90-105.
5. Patil RS, Szolovits P, Schwartz WB. Causal understanding of patient illness in medical diagnosis. In IJCAI. 1981;81:893-9.
6. Wiens J, Guttag J, Horvitz E. Patient risk stratification with time-varying parameters: a multitask learning approach. The Journal of Machine Learning Research. 2016;17(1):2797-819.
7. Henry KE, Hager DN, Pronovost PJ, Saria S. A targeted real-time early warning score (trew score) for septic shock. Science translational medicine. 2015;7(299):299ra122-299ra122.
8. Escobar GJ, Liu VX, Schuler A, et al. Automated identification of adults at risk for in-hospital clinical deterioration. New England Journal of Medicine. 2020;383(20):1951-60.
9. Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017; 542(7639):115-8.
10. McKinney SM, Sieniek M, Godbole V, et al. International evaluation of an AI system for breast cancer screening. Nature. 2020;577(7788):89-94.
11. Yang X, Chen A, Pour Nejatian N, et al. A large language model for electronic health records. NPJ Digit Med. 2022;5:194.
12. Aung YYM, Wong DCS, Ting DSW. The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare. British Medical Bulletin 2021;139:1:4-15.
13. Zhanybekov IZh, Amanbekov AA. The role of artificial intelligence in the study of the discipline pathological anatomy. New technologies and innovations of Kyrgyzstan. 2023;(6). (In Russ.) doi:10.26104/NNTIK.2023.78.79.014.
14. Kung TH, Cheatham M, Medenilla A, et al. Performance of chatgpt on usmle: Potential for ai-assisted medical education usinglarge language models. PLOS Digital Health. 2023;2(2):e0000198.
Review
For citations:
Zheleznova E.A., Vlasov V.V., Vlasova A.V., Depui T.I., Podobed I.V. Potential of artificial intelligence and search engines to successful passing the test part of primary accreditation and primary specialized accreditation. Cardiovascular Therapy and Prevention. 2024;23(4S):4202. (In Russ.) https://doi.org/10.15829/1728-8800-2024-4202. EDN: ZWOPRD
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