From Machine Learning to Generative Artificial Intelligence in Urology: Technological Evolution and Future Perspectives

Artificial intelligence (AI) is being applied across healthcare, including disease diagnosis, personalized treatment, and rehabilitation management. In urology, AI research has evolved from traditional machine learning to advanced deep learning and generative models. This review categorizes these developments into 3 primary domains. First, machine learning has been applied to mobile-based platforms for continuous urinary health monitoring and personalized healthcare. Second, deep learning- based diagnostic systems have improved the identification of conditions such as prostatic hyperplasia, ureteral strictures, and urinary stones, thereby supporting clinical decision-making. Finally, generative AI, including large language models and vision transformers, is reshaping medical image analysis through data augmentation and expanding patient education through natural language interactions. This review examines integrated applications of AI in urological diseases and discusses current research trends and future prospects for a digital healthcare ecosystem in urology.

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Publication Details

Journal
International Neurourology Journal
Published
2026-05-28
DOI
https://doi.org/10.5213/inj.2652134.067
Primary Topic
Kidney Stones and Urolithiasis Treatments
Type
article
Field-Weighted Citation Impact
0.00

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article

From Machine Learning to Generative Artificial Intelligence in Urology: Technological Evolution and Future Perspectives

Yong Gil Na, Sung-Jong Eun, J J Park
International Neurourology Journal
Kidney Stones and Urolithiasis Treatments
article

From Machine Learning to Generative Artificial Intelligence in Urology: Technological Evolution and Future Perspectives

Yong Gil Na, Sung-Jong Eun, J J Park
article en

Abstract

Artificial intelligence (AI) is being applied across healthcare, including disease diagnosis, personalized treatment, and rehabilitation management. In urology, AI research has evolved from traditional machine learning to advanced deep learning and generative models. This review categorizes these developments into 3 primary domains. First, machine learning has been applied to mobile-based platforms for continuous urinary health monitoring and personalized healthcare. Second, deep learning- based diagnostic systems have improved the identification of conditions such as prostatic hyperplasia, ureteral strictures, and urinary stones, thereby supporting clinical decision-making. Finally, generative AI, including large language models and vision transformers, is reshaping medical image analysis through data augmentation and expanding patient education through natural language interactions. This review examines integrated applications of AI in urological diseases and discusses current research trends and future prospects for a digital healthcare ecosystem in urology.

International Neurourology JournalVol. 30(Suppl 1)
Chungnam National University (KR), National IT industry Promotion Agency (KR)
Chungnam National University
Quality Education
Openalex Percentile: Top 6%
Kidney Stones and Urolithiasis Treatments
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