Evidence map›Paper›PMID 37549345›Full record

ArticleCanadian Urological Association journal = Journal de l'Association des urologues du Canada2023

Existing trends and applications of artificial intelligence in urothelial cancer A scoping review.

Shamir Malik, Jeremy Wu, Nicole Bodnariuc, Krishnateja Narayana, Naveen Gupta, Mikail Malik, Jethro C C Kwong, Adree Khondker, Alistair E W Johnson, Girish S Kulkarni

Abstract readScoping Review
PubMed Full text at PMC Publisher
In one paragraph

Article in Canadian Urological Association journal = Journal de l'Association des urologues du Canada, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

10 authors.

Shamir MalikTemerty Faculty of Medicine, University of Toronto, Toronto, ON , Canada.
Jeremy WuTemerty Faculty of Medicine, University of Toronto, Toronto, ON , Canada.
Nicole BodnariucTemerty Faculty of Medicine, University of Toronto, Toronto, ON , Canada.
Krishnateja NarayanaWestern University, London, ON , Canada.
Naveen GuptaGeorgetown University School of Medicine, Georgetown University, Washington, DC, United States.
Mikail MalikTemerty Faculty of Medicine, University of Toronto, Toronto, ON , Canada.
Jethro C C KwongTemerty Centre for AI Research and Education in Medicine, University of Toronto, Toronto, ON , Canada.
Adree KhondkerTemerty Faculty of Medicine, University of Toronto, Toronto, ON , Canada.
Alistair E W JohnsonTemerty Centre for AI Research and Education in Medicine, University of Toronto, Toronto, ON , Canada.
Girish S KulkarniTemerty Centre for AI Research and Education in Medicine, University of Toronto, Toronto, ON , Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThe use of artificial intelligence (AI) in urology is gaining significant traction. While previous reviews of AI applications in urology exist, there have been few attempts to synthesize existing literature on urothelial cancer (UC).

methodsComprehensive searches based on the concepts of "AI" and "urothelial cancer" were conducted in MEDLINE , EMBASE , Web of Science, and Scopus. Study selection and data abstraction were conducted by two independent reviewers. Two independent raters assessed study quality in a random sample of 25 studies with the prediction model risk of bias assessment tool (PROBAST) and the standardized reporting of machine learning applications in urology (STREAM-URO) framework.

resultsFrom a database search of 4581 studies, 227 were included. By area of research, 33% focused on image analysis, 26% on genomics, 16% on radiomics, and 15% on clinicopathology. Thematic content analysis identified qualitative trends in AI models employed and variables for feature extraction. Only 19% of studies compared performance of AI models to non-AI methods. All selected studies demonstrated high risk of bias for analysis and overall concern with Cohen's kappa (k)=0.68. Selected studies met 66% of STREAM-URO items, with k=0.76.

conclusionsThe use of AI in UC is a topic of increasing importance; however, there is a need for improved standardized reporting, as evidenced by the high risk of bias and low methodologic quality identified in the included studies.

Identifiers

PMID37549345
PMCPMC10657228

What OpenQuestion holds

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Registered trials

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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.