Evidence map›Paper›PMID 41615435›Full record

ArticleUrologie (Heidelberg, Germany)2026

[Artificial intelligence in surgical disciplines: Clinical application, advantages, and potential-a Delphi expert consensus].

G Duwe, K Moench, V Kauth, M Angeloni, J Eckhoff, M Görtz, S Hoefert, T D Kocar, L Kollitsch, S Mehralivand and 9 more

Abstract readConsensus StatementEnglish Abstract
In one paragraph

Article in Urologie (Heidelberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

19 authors.

G DuweKlinik und Poliklinik für Urologie und Kinderurologie, Universitätsmedizin der Johannes Gutenberg-Universität Mainz, Langenbeckstr. 1, 55131, Mainz, Deutschland. gregor.duwe@unimedizin-mainz.de.
K MoenchKlinik und Poliklinik für Urologie und Kinderurologie, Universitätsmedizin der Johannes Gutenberg-Universität Mainz, Langenbeckstr. 1, 55131, Mainz, Deutschland.
V KauthKlinik und Poliklinik für Urologie und Kinderurologie, Universitätsmedizin der Johannes Gutenberg-Universität Mainz, Langenbeckstr. 1, 55131, Mainz, Deutschland.
M AngeloniPathologisches Institut, Universitätsklinikum der Friedrich-Alexander-Universität Erlangen, Erlangen, Deutschland.
J EckhoffKlinik für Allgemein‑, Viszeral‑, Thorax- und Transplantationschirurgie, Universitätsklinikum Köln, Köln, Deutschland.
M GörtzNachwuchs-Klinische Kooperationseinheit "Multiparametrische Methoden zur Früherkennung von Prostatakrebs", Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Deutschland.
S HoefertKlinik und Poliklinik für Mund‑, Kiefer- und Gesichtschirurgie, Universitätsklinikum Tübingen, Tübingen, Deutschland.
T D KocarInstitut für Geriatrische Forschung, Universitätsklinikum Ulm, Ulm, Deutschland.
L KollitschAbteilung für Urologie und Andrologie, Klinik Donaustadt, Wien, Österreich.
S MehralivandKlinik und Poliklinik für Urologie, Universitätsklinikum Carl Gustav Carus Dresden, Dresden, Deutschland.
D MercierDeutsches Forschungszentrum für Künstliche Intelligenz (DFKI), Kaiserslautern, Deutschland.
J RudolphKlinik und Poliklinik für Radiologie, LMU Klinikum, LMU München, München, Deutschland.
J RueckelInstitut für Diagnostische und Interventionelle Neuroradiologie, LMU Klinikum, LMU München, München, Deutschland.
R SchönhofKlinik und Poliklinik für Mund‑, Kiefer- und Gesichtschirurgie, Universitätsklinikum Tübingen, Tübingen, Deutschland.
M SondermannKlinik und Poliklinik für Urologie, Universitätsklinikum Carl Gustav Carus Dresden, Dresden, Deutschland.
Caj von KlotKlinik für Urologie und Urologische Onkologie, Medizinische Hochschule Hannover, Hannover, Deutschland.
A ZamzowInstitut für Medizinische Lehre und Ausbildungsforschung, Julius-Maximilians-Universität Würzburg, Würzburg, Deutschland.
J P StruckKlinik für Urologie und Kinderurologie, Universitätsklinikum Brandenburg an der Havel, Brandenburg an der Havel, Deutschland.
H BorgmannKlinik für Urologie und Kinderurologie, Universitätsklinikum Brandenburg an der Havel, Brandenburg an der Havel, Deutschland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) in surgical disciplines has the potential to support all areas of patient care, with the goal of improving treatment quality and patient safety. A group of multidisciplinary experts discussed the current situation as well as steps required to successfully integrate AI into surgical disciplines in the context of a consensus conference at the second Digital Health Summit (Brandenburg an der Havel, Germany) in August 2024.

methodsA modified Delphi procedure was performed with 16 multidisciplinary physicians and scientists on the topic of AI in surgical disciplines and beyond. In two online meetings with subsequent Delphi survey rounds (LimeSurvey) and a final hybrid meeting, individual statements were contributed, discussed, and consented by all 16 participants based on current national clinical guidelines.

resultsFrom a total of 103 submitted statements, 36 statements on reality (n = 12), utopia (n = 13), and opportunities for digital transformation (n = 11) were consented after discussion and modification. We achieved a consensus of at least 75% for all the statements presented, with six of the statements achieving a strong consensus of 100% agreement.

conclusionThe consensus statements show the great potential of AI for improving patient care in surgical disciplines. Challenges such as the lack of digitalization structures and legal frameworks were identified, and practice-oriented proposals for implementation were developed. The need for multidisciplinary cooperation between medical professionals, politics, and industry was emphasized in order to facilitate the German healthcare system remaining competitive for the future, both nationally and internationally.

Indexed as

Artificial IntelligenceDelphi TechniqueDigital HealthGermanyHumansArtificial intelligenceDelphi consensus conferenceDigitalizationLegal frameworkSurgery

Identifiers

PMID41615435
PMCPMC13234076

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.