001     309609
005     20260202120628.0
024 7 _ |a 10.1007/s00120-026-02778-8
|2 doi
024 7 _ |a pmid:41615435
|2 pmid
024 7 _ |a 2731-7064
|2 ISSN
024 7 _ |a 2731-7072
|2 ISSN
037 _ _ |a DKFZ-2026-00259
041 _ _ |a German
082 _ _ |a 610
100 1 _ |a Duwe, G.
|b 0
245 _ _ |a Künstliche Intelligenz in chirurgischen Disziplinen: Einsatz, Nutzen und Potenzial – ein Delphi-Expertenkonsensus.
260 _ _ |a New York]
|c 2026
|b Springer Medizin
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1770021793_1557479
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
500 _ _ |a epub
520 _ _ |a Artificial 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.A 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.From 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.The 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.
536 _ _ |a 315 - Bildgebung und Radioonkologie (POF4-315)
|0 G:(DE-HGF)POF4-315
|c POF4-315
|f POF IV
|x 0
588 _ _ |a Dataset connected to CrossRef, PubMed, , Journals: inrepo02.dkfz.de
650 _ 7 |a Artificial intelligence
|2 Other
650 _ 7 |a Delphi consensus conference
|2 Other
650 _ 7 |a Digitalization
|2 Other
650 _ 7 |a Legal framework
|2 Other
650 _ 7 |a Surgery
|2 Other
700 1 _ |a Moench, K.
|b 1
700 1 _ |a Kauth, V.
|b 2
700 1 _ |a Angeloni, M.
|b 3
700 1 _ |a Eckhoff, J.
|b 4
700 1 _ |a Görtz, M.
|0 P:(DE-He78)0f26d76d27427945f14f0e874d824aa6
|b 5
|u dkfz
700 1 _ |a Hoefert, S.
|b 6
700 1 _ |a Kocar, T. D.
|b 7
700 1 _ |a Kollitsch, L.
|b 8
700 1 _ |a Mehralivand, S.
|b 9
700 1 _ |a Mercier, D.
|b 10
700 1 _ |a Rudolph, J.
|b 11
700 1 _ |a Rueckel, J.
|b 12
700 1 _ |a Schönhof, R.
|b 13
700 1 _ |a Sondermann, M.
|b 14
700 1 _ |a von Klot, Caj
|b 15
700 1 _ |a Zamzow, A.
|b 16
700 1 _ |a Struck, J. P.
|b 17
700 1 _ |a Borgmann, H.
|b 18
773 _ _ |a 10.1007/s00120-026-02778-8
|0 PERI:(DE-600)3123197-4
|p nn
|t Die Urologie
|v nn
|y 2026
|x 2731-7064
909 C O |o oai:inrepo02.dkfz.de:309609
|p VDB
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 5
|6 P:(DE-He78)0f26d76d27427945f14f0e874d824aa6
913 1 _ |a DE-HGF
|b Gesundheit
|l Krebsforschung
|1 G:(DE-HGF)POF4-310
|0 G:(DE-HGF)POF4-315
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-300
|4 G:(DE-HGF)POF
|v Bildgebung und Radioonkologie
|x 0
914 1 _ |y 2026
915 _ _ |a DEAL Springer
|0 StatID:(DE-HGF)3002
|2 StatID
|d 2025-11-11
|w ger
915 _ _ |a DEAL Springer
|0 StatID:(DE-HGF)3002
|2 StatID
|d 2025-11-11
|w ger
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b UROLOGIE : 2022
|d 2025-11-11
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2025-11-11
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2025-11-11
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2025-11-11
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1110
|2 StatID
|b Current Contents - Clinical Medicine
|d 2025-11-11
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1190
|2 StatID
|b Biological Abstracts
|d 2025-11-11
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2025-11-11
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2025-11-11
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2025-11-11
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1050
|2 StatID
|b BIOSIS Previews
|d 2025-11-11
915 _ _ |a IF < 5
|0 StatID:(DE-HGF)9900
|2 StatID
|d 2025-11-11
920 1 _ |0 I:(DE-He78)E250-20160331
|k E250
|l NWG KKE Multiparametrische Methoden zur Früherkennung des Prostatakarzinoms
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-He78)E250-20160331
980 _ _ |a UNRESTRICTED


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21