What neural networks and how a urologist can utilize in his routine tasks: practical tips

Abstract

Artificial intelligence (AI) is becoming an integral part of modern medicine, including urology. This article examines key approaches to implementing AI in diagnostics, treatment, and patient management. The focus is on the potential of neural networks, such as convolutional and recurrent networks, for medical image analysis, outcome prediction, and automation of routine tasks. Particular attention is given to the application of deep learning algorithms in identifying and segmenting urological pathologies in ultrasound, CT, and MRI images. Successful examples of AI use in diagnosing prostate and bladder cancer, predicting complication risks, and developing personalized therapeutic strategies are discussed. The advantages and limitations of AI in urological practice are explored, including the need for high-quality data for model training, challenges in algorithm interpretation, and ethical considerations. The article provides practical recommendations for urologists on integrating AI into clinical activities, emphasizing its role in improving healthcare quality and enhancing decision-making accuracy. The article serves as a practical guide for urologists seeking to integrate AI into clinical practice, emphasizing the importance of combining AI technologies with clinical expertise to optimize the quality of medical care. On the threshold of a new era in urology, those who are the first to master artificial intelligence will not only transform approaches to treatment but also shape the future of the profession.