Abstract:
Early detection of urological oncological diseases, such as prostate, bladder and kidney cancer, is one of the key factors influencing the prognosis and success of treatment. Despite progress in screening methods and clinical diagnostics, a significant proportion of patients still seek help at later stages, when the effectiveness of treatment decreases. Therefore, the development of innovative approaches toearly diagnosis and individual prediction of the course of diseases is critically important for modern medicine. Modern biomarkers allow assessing the risk of developing diseases and detecting them at preclinical stages, which significantly improves treatment outcomes. At the same time, the rapid development of artificial intelligence methods opens up new opportunities for analyzing large volumes of data and creating personalized prediction models. The integration of artificial intelligence and biomarker analysis into clinical practice provides increased accuracy of diagnosis and prognosis, as well as optimization of therapeutic approaches. At the same time, the results of these studies are not systematic and require systematization and coordination. The aim of this study was to investigate modern scientific publications on methods and strategies for diagnosing and predicting the most common urological oncological diseases. To achieve this goal, a search was performed within the Google Scholar scientometric database using keywords. The search was conducted to a depth of 10 years. As a result, 19 publications were taken for analysis. A review of literature sources on modern strategies for early diagnosis and prediction of urological oncological diseases allowsus to highlight several key areas. In particular, the use of biological markers is important, which help detect diseases at early stages. They allow us to assess the risk of pathology development and improve the timeliness of diagnosis, which significantly affects the prognosis and effectiveness of treatment. The use of artificial intelligence technologies, which allow us to analyze large volumes of clinical and genetic data, also attracts attention. Thanks to these technologies, it becomes possible to predict the course of the disease, the risk of complications and the effectiveness of various types of therapy. Machine learning algorithms contribute to the personalization of approaches to patient treatment, optimizing the choice of the best strategy. The literature also highlights the importance of integrating these approaches into clinical practice. However, there are challenges associated with standardizing the methods, validating their effectiveness, and implementing them widely in the healthcare system.Thus, the review shows that the future of early diagnosis and disease prediction depends on the combination of modern biological research and advanced technological solutions aimed at individualizing healthcare.
