Evidence map›Paper›PMID 40221553›Full record

ReviewAnnals of surgical oncology2025

Recent Advances in Artificial Intelligence for Precision Diagnosis and Treatment of Bladder Cancer: A Review.

Xiangxiang Yang, Rui Yang, Xiuheng Liu, Zhiyuan Chen, Qingyuan Zheng

Abstract readReview
PubMed Publisher
In one paragraph

Review in Annals of surgical oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–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

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
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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

5 authors.

Xiangxiang YangDepartment of Urology, Renmin Hospital of Wuhan University, Wuhan, Hubei, People's Republic of China.
Rui YangDepartment of Urology, Renmin Hospital of Wuhan University, Wuhan, Hubei, People's Republic of China.
Xiuheng LiuDepartment of Urology, Renmin Hospital of Wuhan University, Wuhan, Hubei, People's Republic of China.
Zhiyuan ChenDepartment of Urology, Renmin Hospital of Wuhan University, Wuhan, Hubei, People's Republic of China. drchenzy@whu.edu.cn.ORCID http://orcid.org/0000-0002-6820-3678
Qingyuan ZhengDepartment of Urology, Renmin Hospital of Wuhan University, Wuhan, Hubei, People's Republic of China. zqy710890394@whu.edu.cn.ORCID http://orcid.org/0000-0003-4682-3857

Funding

China Primary Health Care Foundation 025Hubei Province Central Guiding Local Science and Technology Development Project 2022BGE232Hubei Province Key Research and Development Project 2020BCB051
6 · The paper itself

Abstract

backgroundBladder cancer is one of the top ten cancers globally, with its incidence steadily rising in China. Early detection and prognosis risk assessment play a crucial role in guiding subsequent treatment decisions for bladder cancer. However, traditional diagnostic methods such as bladder endoscopy, imaging, or pathology examinations heavily rely on the clinical expertise and experience of clinicians, exhibiting subjectivity and poor reproducibility. MATERIALS AND

methodsWith the rise of artificial intelligence, novel approaches, particularly those employing deep learning technology, have shown significant advancements in clinical tasks related to bladder cancer, including tumor detection, molecular subtyping identification, tumor staging and grading, prognosis prediction, and recurrence assessment.

resultsArtificial intelligence, with its robust data mining capabilities, enhances diagnostic efficiency and reproducibility when assisting clinicians in decision-making, thereby reducing the risks of misdiagnosis and underdiagnosis. This not only helps alleviate the current challenges of talent shortages and uneven distribution of medical resources but also fosters the development of precision medicine.

conclusionsThis study provides a comprehensive review of the latest research advances and prospects of artificial intelligence technology in the precise diagnosis and treatment of bladder cancer.

Indexed as

Artificial IntelligencePrecision MedicineUrinary Bladder NeoplasmsDeep LearningHumansPrognosisArtificial intelligenceBladder cancerDeep learningDiagnosisMachine learningPrecision medicine

Identifiers

PMID40221553

What OpenQuestion holds

Textmetadata
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.