{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T19:56:36Z","timestamp":1780343796591,"version":"3.54.1"},"reference-count":55,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2024,10,14]],"date-time":"2024-10-14T00:00:00Z","timestamp":1728864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>The global population is currently at unprecedented levels, with an estimated 7.8 billion people inhabiting the planet. We are witnessing a rise in cancer cases, attributed to improved control of cardiovascular diseases and a growing elderly population. While this has resulted in an increased workload for pathologists, it also presents an opportunity for advancement. The accurate classification of tumors and identification of prognostic and predictive factors demand specialized expertise and attention. Fortunately, the rapid progression of artificial intelligence (AI) offers new prospects in medicine, particularly in diagnostics such as image and surgical pathology. This article explores the transformative impact of AI in the field of uropathology, with a particular focus on its application in diagnosing, grading, and prognosticating various urological cancers. AI, especially deep learning algorithms, has shown significant potential in improving the accuracy and efficiency of pathology workflows. This comprehensive review is dedicated to providing an insightful overview of the primary data concerning the utilization of AI in diagnosing, predicting prognosis, and determining drug responses for tumors of the urinary tract. By embracing these advancements, we can look forward to improved outcomes and better patient care.<\/jats:p>","DOI":"10.3390\/diagnostics14202279","type":"journal-article","created":{"date-parts":[[2024,10,14]],"date-time":"2024-10-14T05:47:58Z","timestamp":1728884878000},"page":"2279","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Artificial Intelligence in Uropathology"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2615-7730","authenticated-orcid":false,"given":"Katia Ramos Moreira","family":"Leite","sequence":"first","affiliation":[{"name":"Laboratory of Medical Investigation, Urology Department, University of S\u00e3o Paulo Medical School, LIM55, Av Dr. Arnando 455, Sao Paulo 01246-903, SP, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Petronio Augusto de Souza","family":"Melo","sequence":"additional","affiliation":[{"name":"Laboratory of Medical Investigation, Urology Department, University of S\u00e3o Paulo Medical School, LIM55, Av Dr. Arnando 455, Sao Paulo 01246-903, SP, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kim, I., Kang, K., Song, Y., and Kim, T.J. 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