THE ROLE OF ARTIFICIAL INTELLIGENCE IN MODERN UROLOGY: A SYSTEMATIC OVERVIEW

Adrian Zagórski; Hubert Kostka; Damian Dolata; Jadwiga Kleinrok; Anna Bereta-Kostaś; Rafał Kuśmider; Małgorzata Leśnik; Patrycja Wierzbowska

doi:10.31435/ijitss.4(48).2025.4330

Authors

Adrian Zagórski 5 Military Clinical Hospital with Polyclinic SPZOZ, Kraków, Poland https://orcid.org/0000-0002-5420-8101
Hubert Kostka 5 Military Clinical Hospital with Polyclinic SPZOZ, Kraków, Poland https://orcid.org/0009-0003-4419-5364
Damian Dolata Independent Public Healthcare Institution of the Ministry of the Interior and Administration in Kraków, Kraków, Poland https://orcid.org/0009-0006-9165-8212
Jadwiga Kleinrok University Hospital in Kraków, Kraków, Poland https://orcid.org/0009-0005-2132-9299
Anna Bereta-Kostaś University Hospital in Kraków, Kraków, Poland https://orcid.org/0009-0007-5664-3119
Rafał Kuśmider Medical University of Silesia, Katowice, Poland https://orcid.org/0009-0003-3580-3495
Małgorzata Leśnik Medical University of Silesia, Katowice, Poland https://orcid.org/0009-0004-4798-6302
Patrycja Wierzbowska Gabriel Narutowicz Municipal Specialist Hospital, Kraków, Poland https://orcid.org/0009-0005-5201-0512

DOI:

https://doi.org/10.31435/ijitss.4(48).2025.4330

Keywords:

urology, Artificial Intelligence, Machine Learning, Prostatic Neoplasms, Diagnostic Imaging, Clinical Decision-Making

Abstract

Introduction and Purpose: Artificial intelligence (AI) is a revolutionary tool assisting diagnostics treatment, and prognosis of treatment outcomes in various medical fields, including urology. The purpose of this review is to outline contemporary uses of AI techniques in clinical urology and evaluate their effect on the quality of patient care, considering limitations and future research directions.

State of Knowledge: AI uses in urology consist of, inter alia, evaluation of radiological and histopathological images (for example, in prostate cancer diagnosis), treatment prediction outcomes (e.g., bladder cancer), individualization of treatment, improvement in surgical planning decisions and assistance in perioperative care. Machine learning algorithms are applied to recognize pathological changes with high accuracy, often like the assessments of experts. Natural language processing (NLP) algorithms are utilized in the analysis of medical documentation and streamlining information flow. Despite quick development, complete integration of AI into daily clinical practice faces barriers related to data quality, model interpretability, and legal and ethical aspects.

Summary: Artificial intelligence has excellent potential for enhancing diagnostic and therapeutic accuracy in urology. Nonetheless, additional clinical research, standardization and validation with multi-center datasets are required. The appropriate implementation of AI in urological practice can lead to personalized, more efficient patient management.

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