Evidence map›Paper›PMID 42252644›Full record

ReviewInternational neurourology journal2026

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

Sung-Jong Eun, Jong Mok Park, Yong Gil Na

Abstract readReview
In one paragraph

Review in International neurourology journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Sung-Jong EunNational IT Industry Promotion Agency, Jincheon, Korea.
Jong Mok ParkDepartment of Urology, Chungnam National University Sejong Hospital, Chungnam National University College of Medicine, Sejong, Korea. jm0113@hanmail.net.
Yong Gil NaDepartment of Urology, Chungnam National University Sejong Hospital, Chungnam National University College of Medicine, Sejong, Korea. yongna@cnu.ac.kr.

Funding

Chungnam National University
6 · The paper itself

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.

Indexed as

Artificial intelligenceDeep learningGenerative artificial intelligenceLarge language modelsMobile healthcareUrological disease

Identifiers

PMID42252644
PMCPMC13248950

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.