USE OF MODERN INFORMATION TECHNOLOGIES AND ARTIFICIAL INTELLIGENCE SYSTEMS IN HEALTHCARE, THE CASE OF CHATGPT: ADVANTAGES AND POTENTIAL RISKS
DOI:
https://doi.org/10.52754/16948742_1(6)_12-2025Keywords:
information technologies, artificial intelligence, ChatGPT, healthcare, medical applications, human nutritionAbstract
The rapid integration of modern information technologies (IT) and artificial intelligence (AI) systems into healthcare has created new opportunities and challenges. This study was conducted to explore various applications of the Chat Generative Pre-Trained Transformer (ChatGPT) in healthcare, particularly in medical education, nutrition science, disease management, and clinical decision-making support. The author examined articles from leading medical journals and databases such as SCOPUS, PubMed, and RSCI. The research highlighted limitations in the use of AI in medicine, with data security concerns being the most common issue. The quantitative data studied demonstrated high accuracy of ChatGPT in disease detection, nutritional sufficiency in generated diet plans, and various medical scenarios. However, finer qualitative aspects, such as user perception, experience, and ethical considerations, might be overlooked. The implementation of AI should focus on data quality and interpretability. AI technologies require long-term impact assessment. ChatGPT shows promising potential in healthcare but requires specialized training for medical applications.
References
Adi Lahat, Sharif, K., Narmin Zoabi, Yonatan Shneor Patt, Sharif, Y., Fisher, L., Shani, U., Mohamad Arow, Levin, R., & Klang, E. (2024). Assessing Generative Pretrained Transformers (GPT) in Clinical Decision-Making: Comparative Analysis of GPT-3.5 and GPT-4. Journal of Medical Internet Research, (26), e54571–e54571. DOI: https://doi.org/10.2196/54571
Ashish Sarraju, Bruemmer, D., Van, E. H., Cho, L., Rodriguez, F., & Laffin, L. J. (2023). Appropriateness of Cardiovascular Disease Prevention Recommendations Obtained From a Popular Online Chat-Based Artificial Intelligence Model. JAMA, (329(10)), 842–842. DOI: https://doi.org/10.1001/jama.2023.1044
Ayoub, M., Ballout, A. A., Zayek, R. A., & Ayoub, N. F. (2023). Mind + Machine: ChatGPT as a Basic Clinical Decisions Support Tool. Cureus, (15(8)). DOI: https://doi.org/10.7759/cureus.43690
Bays, H. E., Fitch, A., Cuda, S., Rickey, E., Hablutzel, J., Coy, R., & Censani, M. (2023). Artificial intelligence and obesity management: An Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) 2023. Obesity Pillars, (6), 100065. DOI: https://doi.org/10.1016/j.obpill.2023.100065
Dave, T., Athaluri, S. A., & Singh, S. (2023). ChatGPT in medicine: an overview of its applications, advantages, limitations, future prospects, and ethical considerations. Frontiers in Artificial Intelligence, (6), 1169595. DOI: https://doi.org/10.3389/frai.2023.1169595
Fergus, S., Botha, M., & Ostovar, M. (2023). Evaluating Academic Answers Generated Using ChatGPT. Journal of Chemical Education, (100(4)), 1672–1675. DOI: https://doi.org/10.1021/acs.jchemed.3c00087
Garcia, M. B. (2023). ChatGPT as a Virtual Dietitian: Exploring Its Potential as a Tool for Improving Nutrition Knowledge. Applied System Innovation, (6(5)), 96–96. DOI: https://doi.org/10.3390/asi6050096
Gilson, A., Safranek, C. W., Huang, T., Socrates, V., Chi, L., Taylor, R. A., & Chartash, D. (2023). How Does ChatGPT Perform on the United States Medical Licensing Examination? The Implications of Large Language Models for Medical Education and Knowledge Assessment. JMIR Medical Education, (9(9)), e45312. DOI: https://doi.org/10.2196/45312
Hieronimus, B., Hammann, S., & Podszun, M. C. (2024). Can the AI tools ChatGPT and Bard generate energy, macro- and micro-nutrient sufficient meal plans for different dietary patterns? Nutrition Research, (128), 105–114. DOI: https://doi.org/10.1016/j.nutres.2024.07.002
Курманалиева, А. О. (2025). Билим берүүдө жасалма интеллект. Жасалма интеллекттин инструменттери (нейрон тармактары), (2(1)), 386–392. DOI: https://doi.org/10.33514/BK-1694-7711-2024-2(1)-386-392
Lee, J., Yoo, I.-S., Kim, J.-H., Won Tae Kim, Hyun Jeong Jeon, Yoo, H.-S., Jae Gwang Shin, Kim, G.-H., Hwang, S., Park, S., & Kim, Y.-J. (2024). Development of AI-generated medical responses using the ChatGPT for cancer patients. Computer Methods and Programs in Biomedicine, (254), 108302–108302. DOI: https://doi.org/10.1016/j.cmpb.2024.108302
Manickam, P., Mariappan, S. A., Murugesan, S. M., Hansda, S., Kaushik, A., Shinde, R., & Thipperudraswamy, S. P. (2022). Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare. Biosensors, (12(8)), 562. DOI: https://doi.org/10.3390/bios12080562
Mijwel, M. M. (2015). History of Artificial Intelligence. (3 (special issue)), 1–8. DOI: https://doi.org/10.13140/RG.2.2.16418.15046
Moritz, S., Bernd Romeike, Christoph Stosch, & Tolks, D. (2023). Generative AI (gAI) in medical education: Chat-GPT and co. PubMed, (40(4)), Doc54–Doc54. DOI: https://doi.org/10.3205/zma001636
Naik, N., Hameed, B. M. Z., Shetty, D. K., Swain, D., Shah, M., Paul, R., Aggarwal, K., Ibrahim, S., Patil, V., Smriti, K., Shetty, S., Rai, B. P., Chlosta, P., & Somani, B. K. (2022). Legal and Ethical Consideration in Artificial Intelligence in Healthcare: Who Takes Responsibility? Frontiers in Surgery, (9(862322)), 1–6. Frontiers. DOI: https://doi.org/10.3389/fsurg.2022.862322
Nino Fijačko, Prosen, G., Abella, B. S., Špela Metličar, & Gregor Štiglic. (2023). Can novel multimodal chatbots such as Bing Chat Enterprise, ChatGPT-4 Pro, and Google Bard correctly interpret electrocardiogram images? Resuscitation, (193), 110009–110009. DOI: https://doi.org/10.1016/j.resuscitation.2023.110009
Niszczota, P., & Rybicka, I. (2023). The credibility of dietary advice formulated by ChatGPT: robo-diets for people with
food allergies. Nutrition, (112), 112076. DOI: https://doi.org/10.1016/j.nut.2023.112076
Sallam, M. (2023). ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns. Healthcare, (11(6)), 887. DOI: https://doi.org/10.3390/healthcare11060887
Shaderkin I.A. (2021). Weaknesses of artificial intelligence in medicine. Russian Journal of Telemedicine and E-Health, (7(2)), 50–52. DOI: https://doi.org/10.29188/2712-9217-2021-7-2-50-52
Шадеркина, В. А. (2024). ChatGPT в медицине: возможности и ограничения. Российский журнал телемедицины
и электронного здравоохранения. Jtelemed.ru. [Электронный ресурс]. URL:
https://jtelemed.ru/article/chatgpt-v-medicine-vozmozhnosti-i-ogranichenija
Skryd, A. & Lawrence, K. (2024). ChatGPT as a Tool for Medical Education and Clinical Decision-Making on the Wards: Case Study. JMIR Formative Research, (8), e51346–e51346. DOI: https://doi.org/10.2196/51346
Sng, G. G. R., Tung, J. Y. M., Lim, D. Y. Z. & Bee, Y. M. (2023). Potential and Pitfalls of ChatGPT and Natural-Language Artificial Intelligence Models for Diabetes Education. Diabetes Care, (46). DOI: https://doi.org/10.2337/dc23-0197
Tanaka, Y., Nakata, T., Ko Aiga, Takahide Etani, Muramatsu, R., Katagiri, S., Kawai, H., Fumiya Higashino, Enomoto, M., Noda, M., Mitsuhiro Kometani, Takamura, M., Yoneda, T., Hiroaki Kakizaki, & Nomura, A. (2024). Performance of Generative Pretrained Transformer on the National Medical Licensing Examination in Japan. PLOS Digital Health, (3(1)), e0000433–e0000433. DOI: https://doi.org/10.1371/journal.pdig.0000433
Wang, L.-C., Zhang, H., Ginsberg, N., Ban, A. N., Kooman, J. P., & Kotanko, P. (2024). Application of ChatGPT to Support Nutritional Recommendations for Dialysis Patients – A Qualitative and Quantitative Evaluation. Journal of Renal Nutrition, (34(6)). DOI: https://doi.org/10.1053/j.jrn.2024.09.001
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Алия Базиева, Асель Камчиева

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.