Evidence map›Paper›PMID 37568397›Full record

ReviewJournal of clinical medicine2023

The Present and Future of Artificial Intelligence in Urological Cancer.

Xun Liu, Jianxi Shi, Zhaopeng Li, Yue Huang, Zhihong Zhang, Changwen Zhang

Abstract readReview
In one paragraph

Review in Journal of clinical medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. 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

6 authors.

Xun LiuTianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin 300211, China.
Jianxi ShiTianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin 300211, China.ORCID 0009-0004-6769-8471
Zhaopeng LiTianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin 300211, China.
Yue HuangTianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin 300211, China.
Zhihong ZhangTianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin 300211, China.
Changwen ZhangTianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin 300211, China.

Funding

National Key Research and Development Program of China 2021YFC2009300, 2021YFC2009303
6 · The paper itself

Abstract

Artificial intelligence has drawn more and more attention for both research and application in the field of medicine. It has considerable potential for urological cancer detection, therapy, and prognosis prediction due to its ability to choose features in data to complete a particular task autonomously. Although the clinical application of AI is still immature and faces drawbacks such as insufficient data and a lack of prospective clinical trials, AI will play an essential role in individualization and the whole management of cancers as research progresses. In this review, we summarize the applications and studies of AI in major urological cancers, including tumor diagnosis, treatment, and prognosis prediction. Moreover, we discuss the current challenges and future applications of AI.

Indexed as

artificial intelligenceimagingpathologytreatmenturological cancer

Identifiers

PMID37568397
PMCPMC10419644

What OpenQuestion holds

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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.