Evidence map›Paper›PMID 36729176›Full record

ReviewUrologie (Heidelberg, Germany)2023

[Artificial intelligence in urology-opportunities and possibilities].

Radu Alexa, Jennifer Kranz, Christoph Kuppe, Sikander Hayat, Marco Hoffmann, Matthias Saar

Abstract readEnglish AbstractReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Urologie (Heidelberg, Germany), 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. Observational
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.

Radu AlexaKlinik für Urologie und Kinderurologie, Uniklinik RWTH Aachen, Pauwelsstr. 30, 52074, Aachen, Deutschland. ralexa@ukaachen.de.
Jennifer KranzKlinik für Urologie und Kinderurologie, Uniklinik RWTH Aachen, Pauwelsstr. 30, 52074, Aachen, Deutschland.
Christoph KuppeInstitut für Experimentelle Innere Medizin und Systembiologie, Uniklinik RWTH Aachen, Aachen, Deutschland.
Sikander HayatInstitut für Experimentelle Innere Medizin und Systembiologie, Uniklinik RWTH Aachen, Aachen, Deutschland.
Marco HoffmannKlinik für Urologie und Kinderurologie, Uniklinik RWTH Aachen, Pauwelsstr. 30, 52074, Aachen, Deutschland.
Matthias SaarKlinik für Urologie und Kinderurologie, Uniklinik RWTH Aachen, Pauwelsstr. 30, 52074, Aachen, Deutschland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The use of artificial intelligence (AI) in urology can contribute to a significant improvement with regard to individualization of diagnostics and therapy as well as healthcare cost reduction. The potential applications and advantages of AI in medicine are often underestimated or incompletely understood. This makes it difficult to conceptually solve relevant medical problems using AI. With current advances in computer science, multiple, highly complex nonmedical processes have already been studied and optimized in an automated fashion. The development of AI models, if applied correctly, can lead to more effective processing and analysis of patient-related data and correspondingly optimized diagnosis and therapy of urological patients. In this review, the current status on the application of AI in medicine and its opportunities and possibilities in urology are presented from a conceptual perspective using practical examples.

Indexed as

Artificial IntelligenceUrologyHumansDeep learningKidneyMachine LearningNeuronal networksProstateUrinary bladder

Identifiers

PMID36729176
PMCPMC10073044

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.