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Artificial intelligence and neural networks in urology DOI: 10.29188/2222-8543-2023-16-2-32-37
- Shchamkhalova K.K. – student, N. Lopatkin Scientific Research Institute of Urology and Interventional Radiology – branch of the National Medical Research Centre of Radiology of Ministry of Health of the Russian Federation; Moscow, Russia
- Merinov D.S. – Dr. Sci., Head of the Department of Endourology, Scientific Research Institute of Urology and Interventional Radiology n.a. N.A. Lopatkin – branch of the National Medical Research Centre of Radiology of Ministry of Health of the Russian Federation; Moscow, Russia; RSCI Author ID 636113; https://orcid.org/0000-0001-5966-9233
- Artemov A.V. – Ph.D., Head of the operating unit with a sterilization room, N. Lopatkin Scientific Research Institute of Urology and Interventional Radiology – branch of the National Medical Research Centre of Radiology of Ministry of Health of the Russian Federation; Moscow, Russia; RSCI Author ID 787885
- Gurbanov Sh.Sh. – Ph.D., Senior Researcher at the Department of Endourology, N. Lopatkin Scientific Research Institute of Urology and Interventional Radiology – branch of the National Medical Research Radiological Centre, Moscow, Russia; RSCI Author ID 636203
428 Introduction. The review is devoted to the analysis of the world experience in the use of artificial intelligence in medicine and urology.
Materials and methods. The review was conducted on the basis of data published in the PubMed, Scientific Electronic Library eLibrary.ru databases and on the professional medical associations websites. The article contains 43 scientific publications.
Results. In this literature review, the types of neural networks and the features of their application are considered in detail. Special attention is paid to the use of artificial intelligence in medical imaging, particularly in urology. Effectiveness and accuracy of developing the connectionism method application in artificial neural network (ANN) in recent publications are presented. Advantages and disadvantages of artificial intelligence for a predictive model are outlined. We also considered the main mistakes displayed in publications regarding unreliable or poorly created neural networks and studies in the use of ANNs for clinical practice.
Conclusion. The use of neural networks as a tool in the daily clinical practice of a specialist is still limited. The main problems are still the unreliability of the created models and lack of easy-to-use algorithms.
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