Evidence map›Paper›PMID 30569194›Full record

ReviewDer Urologe. Ausg. A2019

[Artificial intelligence and neural networks in urology].

Christoph-Alexander J von Klot, Markus A Kuczyk

Abstract readReview
PubMed Publisher
In one paragraph

Review in Der Urologe. Ausg. A, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. [Digitalization in urology-challenge and opportunity].Urologie (Heidelberg, Germany) · 2023
    Review
  2. Review
  3. Review
  4. [Application of artificial intelligence in thoracic surgery].Der Chirurg; Zeitschrift fur alle Gebiete der operativen Medizen · 2020
    Review
  5. 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

2 authors.

Christoph-Alexander J von KlotKlink für Urologie und Urologische Onkologie der Medizinischen Hochschule Hannover, Carl-Neuberg-Str. 1, 30625, Hannover, Deutschland. klot.christoph@mh-hannover.de.
Markus A KuczykKlink für Urologie und Urologische Onkologie der Medizinischen Hochschule Hannover, Carl-Neuberg-Str. 1, 30625, Hannover, Deutschland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Knowledge of artificial intelligence will become essential in urology and medicine in the future. Today, computer-aided solutions are used for higher-dimensioned data or complex questions. In urological research, but also in clinical application, artificial neural networks (ANN) have been increasingly used for years and are continuously being developed. ANNs are already successfully used in urology in the field of image recognition in radiology and pathology. ANNs can potentially play a role in decision-making of modern cancer treatment. Today, access to ANN has become comparatively easy.

Indexed as

Artificial IntelligenceNeural Networks, ComputerUrologyForecastingArtificial neuronal networksBig DataImage recognitionMatrix multiplicationMultiparameter magnetic resonance imaging

Identifiers

PMID30569194

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.