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PubMed-MEDLINE searches identified studies addressing grade prediction, improved diagnosis, prognosis and recurrence in prostate cancer, radiomics-driven tumor aggressiveness and outcome prediction in bladder cancer, and benign–malignant differentiation plus grading and subtyping in kidney cancer. Findings indicate AI-based methods are often superior or comparable to traditional approaches.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/applications-of-artificial-intelligence-in-urologic-oncology/330568/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/applications-of-artificial-intelligence-in-urologic-oncology/330568.png","ImageObject",300,407,{"name":92,"@type":93},"Aurora","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-23","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the purpose of this review on AI in urologic oncology?","Question",{"text":112,"@type":113},"The review aims to comprehensively summarize recent applications of artificial intelligence in urologic oncology, using evidence from studies on machine learning and deep learning models related to relevant cancers.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which AI approaches are defined in the introduction?",{"text":117,"@type":113},"The introduction explains machine learning (ML), deep learning (DL), computer vision, natural language processing, and robotics, including how each is used or expected to be used in medical contexts.",{"name":119,"@type":110,"acceptedAnswer":120},"What key areas of prostate, bladder, and kidney cancer does the review cover?",{"text":121,"@type":113},"For prostate cancer, studies focus on grade prediction, improved diagnosis, and predicting outcomes and recurrence. 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[https://doi.org/10.4111/icu.20230435](https://doi.org/10.4111/icu.20230435)[ ](https://doi.org/10.4111/icu.20230435)[pISSN 2466-0493](pISSN 2466-0493) • eISSN 2466-054X  \nApplications of artificial intelligence in urologic oncology  \nSahyun Pak1,*, Sung Gon Park1,*, Jeonghyun Park2, Sung Tae Cho1, Young Goo Lee1, Hanjong Ahn3  \n1Department of Urology, Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Seoul, 2STARLABS Corp., Seoul, 3Department of Urology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea  \nPurpose: With the recent rising interest in artificial intelligence (AI) in medicine, many studies have explored the potential and usefulness of AI in urological diseases. This study aimed to comprehensively review recent applications of AI in urologic oncology. Materials and Methods: We searched the PubMed-MEDLINE databases for articles in English on machine learning (ML) and deep learning (DL) models related to general surgery and prostate, bladder, and kidney cancer. The search terms were a combination of keywords, including both “urology” and “artificial intelligence” with one of the following: “machine learning,”“deep learning,”“neural network,”“renal cell carcinoma,”“kidney cancer,”“urothelial carcinoma,”“bladder cancer,”“prostate cancer,” and “robotic surgery.”Results: A total of 58 articles were included. The studies on prostate cancer were related to grade prediction, improved diagnosis, and predicting outcomes and recurrence. The studies on bladder cancer mainly used radiomics to identify aggressive tumors and predict treatment outcomes, recurrence, and survival rates. Most studies on the application of ML and DL in kidney cancer were focused on the differentiation of benign and malignant tumors as well as prediction of their grade and subtype. Most studies suggested that methods using AI may be better than or similar to existing traditional methods.  \nConclusions: AI technology is actively being investigated in the field of urological cancers as a tool for diagnosis, prediction of prognosis, and decision-making and is expected to be applied in additional clinical areas soon. Despite technological, legal, and ethical concerns, AI will change the landscape of urological cancer management.  \nKeywords: Artificial intelligence; Deep learning; Machine learning; Urological cancer  \nThis is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ([http://creativecommons.org/licenses/by-nc/4.0](http://creativecommons.org/licenses/by-nc/4.0)) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.  \nINTRODUCTION  \nArtificial intelligence (AI) is a field of technology that uses computer systems to mimic intelligent behaviors such as human learning, reasoning, problem-solving, and cognition [1]. AI has been studied for its potential and is already being utilized in various industries. AI is also expected to bring revolutionary changes to health care, especially in  \nfacilitating clinical decision-making [2,3]. AI can be used for various purposes, including aiding in diagnosis in outpatient settings, establishing treatment plans, performing predictive analytics, remote monitoring of patients through wearable devices and mobile apps, and optimizing electronic medical records [3-5].  \nTo understand AI in the medical field, it is necessary to explore the following concepts. (1) Machine learning (ML):  \nReceived: 30 December, 2023 • Revised: 24 February, 2024 • Accepted: 11 March, 2024 • Published online: 30 April, 2024  \nCorresponding Author: Hanjong Ahn  [https://orcid.org/0000-0001-7608-5352](https://orcid.org/0000-0001-7608-5352)  \nDepartment of Urology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea TEL: +82-2-3010-3733, FAX: +82","cbCaiqcTFGqw544S","https://ap.wps.com/l/cbCaiqcTFGqw544S","pdf",415484,15,"English","# Introduction\n## Machine learning (ML)\n## Deep learning (DL)\n## Computer vision\n## Natural language processing\n## Robotics\n# Purpose and methods\n# Results\n# Conclusions","[{\"question\":\"What is the purpose of this review on AI in urologic oncology?\",\"answer\":\"The review aims to comprehensively summarize recent applications of artificial intelligence in urologic oncology, using evidence from studies on machine learning and deep learning models related to relevant cancers.\"},{\"question\":\"Which AI approaches are defined in the introduction?\",\"answer\":\"The introduction explains machine learning (ML), deep learning (DL), computer vision, natural language processing, and robotics, including how each is used or expected to be used in medical contexts.\"},{\"question\":\"What key areas of prostate, bladder, and kidney cancer does the review cover?\",\"answer\":\"For prostate cancer, studies focus on grade prediction, improved diagnosis, and predicting outcomes and recurrence. For bladder cancer, studies largely use radiomics to identify aggressive tumors and predict treatment outcomes, recurrence, and survival. For kidney cancer, studies focus on differentiating benign and malignant tumors and predicting grade and subtype.\"}]","Applications of artificial intelligence in urologic oncology | PDF",1789989436,38]