Evidence map›Paper›PMID 40629505›Full record

ReviewChinese medical journal2025

Artificial intelligence in prostate cancer.

Wei Li, Ruoyu Hu, Quan Zhang, Zhangsheng Yu, Longxin Deng, Xinhao Zhu, Yujia Xia, Zijian Song, Alessia Cimadamore, Fei Chen and 4 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Chinese medical journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Review
  5. Article
  6. Review
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

14 authors.

Wei LiDepartment of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China.
Ruoyu HuDepartment of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China.
Quan ZhangDepartment of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China.
Zhangsheng YuDepartment of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.
Longxin DengDepartment of Urology, The First Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai 200433, China.
Xinhao ZhuDepartment of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China.
Yujia XiaDepartment of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.
Zijian SongDepartment of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China.
Alessia CimadamoreInstitute of Pathological Anatomy, Department of Medicine, University of Udine, Udine, Italy.
Fei ChenDepartment of Pathology and Laboratory Medicine, New York University Grossman School of Medicine and NYU Langone Health, New York, NY, USA.
Antonio Lopez-BeltranDepartment of Surgery, Cordoba University Medical School, Cordoba, Spain.
Rodolfo MontironiMolecular Medicine and Cell Therapy Foundation, Polytechnic University of the Marche Region, Ancona, Italy.
Liang ChengDepartment of Pathology and Laboratory Medicine, Department of Surgery (Urology), Brown University Warren Alpert Medical School, the Legorreta Cancer Center at Brown University, and Brown University Health, Providence, RI, USA.
Rui ChenDepartment of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

abstractProstate cancer (PCa) ranks as the second most prevalent malignancy among men worldwide. Early diagnosis, personalized treatment, and prognosis prediction of PCa play a crucial role in improving patients' survival rates. The advancement of artificial intelligence (AI), particularly the utilization of deep learning (DL) algorithms, has brought about substantial progress in assisting the diagnosis, treatment, and prognosis prediction of PCa. The introduction of the foundation model has revolutionized the application of AI in medical treatment and facilitated its integration into clinical practice. This review emphasizes the clinical application of AI in PCa by discussing recent advancements from both pathological and imaging perspectives. Furthermore, it explores the current challenges faced by AI in clinical applications while also considering future developments, aiming to provide a valuable point of reference for the integration of AI and clinical applications.

Indexed as

Artificial IntelligenceProstatic NeoplasmsDeep LearningHumansMalePrognosisArtificial intelligenceFoundation modelImagingPathologyProstate cancerWhole-slide image

Identifiers

PMID40629505
PMCPMC12321470

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.