Applications of Neural Networks and Machine Learning Techniques in Medicine and Urology

Abstract

The use of Artificial Intelligence, especially machine learning and artificial neural networks, has dramatically increased in urology, assisting in innovative ways for diagnosis, prognosis, and treatment planning. This paper presents an up-to-date review of AI advances in the field of urology that pertain to its imaging aspects, particularly regarding the diagnosis of prostate cancer, kidney stones, and bladder cancer. The deep learning methods, especially convolutional neural networks, proved to be very effective in many medical imaging tasks, such as automated abnormal growth detection, organ segmentation, etc. Additionally, deep learning systems have performed well in predicting a patient’s outcome, including post-operative complications and recovery. Nevertheless, the progress made greatly differs from the goals set, and AI’s integration into clinical practice remains an unmet need due to obstacles posed by inefficient datasets and the opacity of some AI algorithms.This paper also discusses the key challenges in implementing AI tools in urology, as well as the potential for future research to enhance the accuracy, interpretability, and clinical applicability of AI-driven solutions. Ultimately, AI is poised to play a transformative role in urology, offering the potential for more personalized, efficient, and precise patient care.