Журнал «Почки» Том 13, №3, 2024

Штучний інтелект (ШІ) швидко змінює галузь нефрології, пропонуючи інноваційні рішення, які покращують діагностику, лікування й догляд за пацієнтами. У цьому огляді літератури досліджуються icнуючі та потенційні варіанти застосування штучного інтелекту в різних сферах нефрології. Обговорюється прогрес у ранній діагностиці, персоналізованому плануванні лікування, замісній терапії нирок і транс­плантаційній нефрології, що став можливим завдяки ШІ. Крім того, вивчається, як штучний інтелект покращує догляд за пацієнтами завдяки дистанційному моніторингу, телемедицині та віртуальним помічникам. Незважаючи на величезні перспективи ШІ, у цьому огляді також обговорюються етичні, нормативні й технічні проблеми, що супроводжують його інтеграцію в клінічну практику. Підкреслюючи перетворювальний потенціал ШІ в нефрології, ми акцентуємо увагу на необхідності подальших досліджень та співпраці для повної реалізації його переваг у покращенні результатів лікування нирок.

Artificial intelligence (AI) is rapidly transforming the landscape of nephrology, offering innovative solutions that enhance diagnosis, treatment, and patient care. This literature review explores the current and potential applications of AI across various domains within nephrology. We discuss AI-driven advancements in early diagnosis, personalized treatment planning, renal replacement therapy, and transplant nephrology. Furthermore, we examine how AI enhances patient care through remote monitoring, telehealth, and virtual assistants. While the promise of AI is immense, this review also addresses the ethical, regulatory, and technical challenges that accompany its integration into clinical practice. By highlighting the transformative potential of AI in nephrology, we underscore the need for continued research and collaboration to fully realize its benefits in improving kidney health outcomes.

штучний інтелект; нефрологія; хронічна хвороба нирок; машинне навчання; персоналізована медицина

artificial intelligence; nephrology; chronic kidney disease; machine learning; personalized medicine

1. Briganti, G., Le Moine, O. Artificial Intelligence in Medicine: Today and Tomorrow. Frontiers in medicine. 2020. 7. 27. doi: 10.3389/fmed.2020.00027.
2. Bekbolatova, M., Mayer, J., Ong, C.W., Toma, M. Transformative Potential of AI in Healthcare: Definitions, Applications, and Navigating the Ethical Landscape and Public Perspectives. Healthcare (Basel, Switzerland). 2024. 12(2). 125. doi: 10.3390/healthcare12020125.
3. Umme Habiba, S. et al. Early Prediction of Chronic Kidney Disease Using Machine Learning Algorithms with Feature Selection Techniques. In: Mahmud, M., Ben-Abdallah, H., Kaiser, M.S., Ahmed, M.R., Zhong, N. (eds) Applied Intelligence and Informatics. AII 2023. Communications in Computer and Information Science. 2024. 2065. doi: 10.1007/978-3-031-68639-9_14.
4. Chiu, Y.L., Jhou, M.J., Lee, T.S., Lu, C.J., Chen, M.S. Health Data-Driven Machine Learning Algorithms Applied to Risk Indicators Assessment for Chronic Kidney Disease. Risk management and healthcare policy. 2021. 14. 4401-4412. doi: 10.2147/RMHP.S319405.
5. Deepa, B., Murugappan, M., Sumithra, M.G., Mahmud, M., Al-Rakhami, M.S. Pattern Descriptors Orientation and MAP Firefly Algorithm Based Brain Pathology Classification Using Hybridized Machine Learning Algorithm. IEEE Access. 2022. 10. 3848-3863. doi: 10.1109/access.2021.3100549.
6. Fabietti, M., Mahmud, M., Lotfi, A. Machine Learning in Analysing Invasively Recorded Neuronal Signals: Available Open Access Data Sources. Lecture notes in computer science. 2020. 151-162. doi: 10.1007/978-3-030-59277-6_14.
7. Topol E.J. High-performance medicine: the convergence of human and artificial intelligence. Nature medicine. 2019. 25(1). 44-56. doi: 10.1038/s41591-018-0300-7.
8. Shickel, B., Tighe, P.J., Bihorac, A., Rashidi, P. Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis. IEEE journal of biomedical and health informatics. 2018. 22(5). 1589-1604. doi: 10.1109/JBHI.2017.2767063.
9. Parekh, A.E., Shaikh, O.A., Simran, Manan, S., Hasibuzzaman, M.A. Artificial intelligence (AI) in personalized medicine: AI-generated personalized therapy regimens based on genetic and medical history: short communication. Annals of medicine and surgery (2012). 2023. 85(11). 5831-5833. doi: 10.1097/MS9.0000000000001320.
10. Fayos De Arizón, L., Viera, E.R., Pilco, M., Perera, A., De Maeztu, G., et al. Artificial intelligence: a new field of knowledge for nephrologists? Clinical kidney journal. 2023. 16(12). 2314-2326. doi: 10.1093/ckj/sfad182.
11. Nakamoto, H., Aoyagi, R., Kusano, T. et al. Peritoneal dialysis care by using artificial intelligence (AI) and information techno–logy (IT) in Japan and expectations for the future. Ren Replac Ther. 2023. 9. 31. doi: 10.1186/s41100-023-00479-y.
12. Zamanzadeh, D., Feng, J., Petousis, P., Vepa, A., Sarrafzadeh, M., et al. Data-driven prediction of continuous renal replacement therapy survival. Nature communications. 2024. 15(1). 5440. doi: 10.1038/s41467-024-49763-3.
13. AI in Remote Patient Monitoring: The Top 4 Use Cases in 2024 (2024, March 11). HealthSnap, Inc. Available from: https://healthsnap.io/ai-in-remote-patient-monitoring-the-top-4-use-cases-in-2024/.
14. Isaza-Ruget, M.A., Yomayusa, N., González, C.A., Alvarado, H.C., de Oro V.F.A., et al. Predicting chronic kidney disease progression with artificial intelligence. BMC nephrology. 2024. 25(1). 148. doi: 10.1186/s12882-024-03545-7.
15. Seyahi, N., Ozcan, S.G. Artificial intelligence and kidney transplantation. World journal of transplantation. 2021. 11(7). 277-289. doi: 10.5500/wjt.v11.i7.277.
16. Ramalhete, L., Almeida, P., Ferreira, R., Abade, O., Teixeira, C., Araújo, R. Revolutionizing Kidney Transplantation: Connec–ting Machine Learning and Artificial Intelligence with Next-Generation Healthcare — From Algorithms to Allografts. BioMedInformatics. 2024. 4(1). 673-689. doi: 10.3390/biomedinformatics4010037.
17. Peloso, A., Naesens, M., Thaunat, O. The Dawn of a New Era in Kidney Transplantation: Promises and Limitations of Artificial Intelligence for Precision Diagnostics. Transplant international: official journal of the European Society for Organ Transplantation. 2023. 36. 12010. doi: 10.3389/ti.2023.12010.
18. Bokhari, S.F.H. Artificial Intelligence and Robotics in Transplant Surgery: Advancements and Future Directions. Cureus. 2023. 15(8). e43975. doi: 10.7759/cureus.43975.
19. Badrouchi, S., Bacha, M.M., Ahmed, A. et al. Predicting long-term outcomes of kidney transplantation in the era of artificial intelligence. Sci Rep. 2023. 13. 21273. doi: 10.1038/s41598-023-48645-w.
20. Burrell, D.N. Dynamic Evaluation Approaches to Telehealth Technologies and Artificial Intelligence (AI) Telemedicine Applications in Healthcare and Biotechnology Organizations. Merits. 2023. 3(4). 700-721. doi: 10.3390/merits3040042.
21. Milne-Ives, M., De Cock, C., Lim, E., Shehadeh, M.H., De Pennington, N., et al. The Effectiveness of Artificial Intelligence Conversational Agents in Health Care: Systematic Review. Journal of Medical Internet Research. 2020. 22(10). e20346. doi: 10.2196/20346.
22. Ayers, J.W., Poliak, A., Dredze, M., Leas, E.C., Zhu, Z., et al. Comparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media Forum. JAMA internal medicine. 2023. 183(6). 589-596. doi: 10.1001/jamainternmed.2023.1838.
23. Brady, A.P., Allen, B., Chong, J., Kotter, E., Kottler, N., et al. Developing, purchasing, implementing and monitoring AI tools in radiology: Practical considerations. A multi-society statement from the ACR, CAR, ESR, RANZCR & RSNA. Journal of medical ima–ging and radiation oncology. 2024. 68(1). 7-26. doi: 10.1111/1754-9485.13612.
24. Spiekermann, S. IEEE P7000 — The First Global Standard Process for Addressing Ethical Concerns in System Design. 2017. doi: 10.3390/is4si-2017-04084.
25. Gupta, D.S., Mazumdar, N., Nag, A. et al. Secure data authentication and access control protocol for industrial healthcare system. J Ambient Intell Human Comput. 2023. 14. 4853-4864. doi: 10.1007/s12652-022-04370-2.
26. Eichelberg, M., Kleber, K., Kämmerer, M. Cybersecurity in PACS and Medical Imaging: an Overview. J Digit Imaging. 2020. 33. 1527-1542. doi: 10.1007/s10278-020-00393-3.
27. Savoia, M., Tripepi, G., Goethel-Paal, B., Baró Salvador, M.E., Ponce, P., et al. European Nephrologists’ Attitudes toward the Application of Artificial Intelligence in Clinical Practice: A Comprehensive Survey. Blood purification. 2024. 53(2). 80-87. doi: 10.1159/000534604.
28. Badrouchi, S., Bacha, M.M., Hedri, H., Ben Abdallah, T., Abderrahim, E. Toward generalizing the use of artificial intelligence in nephrology and kidney transplantation. Journal of nephrology. 2023. 36(4). 1087-1100. doi: 10.1007/s40620-022-01529-0.