000168727 001__ 168727
000168727 005__ 20240229133614.0
000168727 0247_ $$2doi$$a10.1016/j.euf.2021.04.006
000168727 0247_ $$2pmid$$apmid:33941503
000168727 0247_ $$2altmetric$$aaltmetric:105038884
000168727 037__ $$aDKFZ-2021-01031
000168727 041__ $$aEnglish
000168727 082__ $$a610
000168727 1001_ $$aCollins, Justin W$$b0
000168727 245__ $$aEthical implications of AI in robotic surgical training: A Delphi consensus statement.
000168727 260__ $$aAmsterdam$$bElsevier$$c2022
000168727 3367_ $$2DRIVER$$aarticle
000168727 3367_ $$2DataCite$$aOutput Types/Journal article
000168727 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1655291605_9349$$xReview Article
000168727 3367_ $$2BibTeX$$aARTICLE
000168727 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000168727 3367_ $$00$$2EndNote$$aJournal Article
000168727 500__ $$a2022 Mar;8(2):613-622
000168727 520__ $$aAs the role of AI in healthcare continues to expand there is increasing awareness of the potential pitfalls of AI and the need for guidance to avoid them.To provide ethical guidance on developing narrow AI applications for surgical training curricula. We define standardised approaches to developing AI driven applications in surgical training that address current recognised ethical implications of utilising AI on surgical data. We aim to describe an ethical approach based on the current evidence, understanding of AI and available technologies, by seeking consensus from an expert committee.The project was carried out in 3 phases: (1) A steering group was formed to review the literature and summarize current evidence. (2) A larger expert panel convened and discussed the ethical implications of AI application based on the current evidence. A survey was created, with input from panel members. (3) Thirdly, panel-based consensus findings were determined using an online Delphi process to formulate guidance. 30 experts in AI implementation and/or training including clinicians, academics and industry contributed. The Delphi process underwent 3 rounds. Additions to the second and third-round surveys were formulated based on the answers and comments from previous rounds. Consensus opinion was defined as ≥ 80% agreement.There was 100% response from all 3 rounds. The resulting formulated guidance showed good internal consistency, with a Cronbach alpha of >0.8. There was 100% consensus that there is currently a lack of guidance on the utilisation of AI in the setting of robotic surgical training. Consensus was reached in multiple areas, including: 1. Data protection and privacy; 2. Reproducibility and transparency; 3. Predictive analytics; 4. Inherent biases; 5. Areas of training most likely to benefit from AI.Using the Delphi methodology, we achieved international consensus among experts to develop and reach content validation for guidance on ethical implications of AI in surgical training. Providing an ethical foundation for launching narrow AI applications in surgical training. This guidance will require further validation.As the role of AI in healthcare continues to expand there is increasing awareness of the potential pitfalls of AI and the need for guidance to avoid them.In this paper we provide guidance on ethical implications of AI in surgical training.
000168727 536__ $$0G:(DE-HGF)POF4-315$$a315 - Bildgebung und Radioonkologie (POF4-315)$$cPOF4-315$$fPOF IV$$x0
000168727 588__ $$aDataset connected to CrossRef, PubMed, , Journals: inrepo01.inet.dkfz-heidelberg.de
000168727 650_7 $$2Other$$aArtificial intelligence
000168727 650_7 $$2Other$$aComputer vision
000168727 650_7 $$2Other$$aDeep learning
000168727 650_7 $$2Other$$aGDPR
000168727 650_7 $$2Other$$aLearning algorithms
000168727 650_7 $$2Other$$aNatural language processing
000168727 650_7 $$2Other$$abiases
000168727 650_7 $$2Other$$acurriculum development
000168727 650_7 $$2Other$$adata protection
000168727 650_7 $$2Other$$amachine learning
000168727 650_7 $$2Other$$anarrow AI
000168727 650_7 $$2Other$$apredictive analytics
000168727 650_7 $$2Other$$aprivacy
000168727 650_7 $$2Other$$arisk prediction
000168727 650_7 $$2Other$$asurgical education
000168727 650_7 $$2Other$$atraining
000168727 650_7 $$2Other$$atransparency
000168727 7001_ $$aMarcus, Hani J$$b1
000168727 7001_ $$aGhazi, Ahmed$$b2
000168727 7001_ $$aSridhar, Ashwin$$b3
000168727 7001_ $$aHashimoto, Daniel$$b4
000168727 7001_ $$aHager, Gregory$$b5
000168727 7001_ $$aArezzo, Alberto$$b6
000168727 7001_ $$aJannin, Pierre$$b7
000168727 7001_ $$0P:(DE-He78)26a1176cd8450660333a012075050072$$aMaier-Hein, Lena$$b8$$udkfz
000168727 7001_ $$0P:(DE-He78)523be43f52702f8ab3e8b2c8166c87fa$$aMarz, Keno$$b9$$udkfz
000168727 7001_ $$aValdastri, Pietro$$b10
000168727 7001_ $$aMori, Kensaku$$b11
000168727 7001_ $$aElson, Daniel$$b12
000168727 7001_ $$aGiannarou, Stamatia$$b13
000168727 7001_ $$aSlack, Mark$$b14
000168727 7001_ $$aHares, Luke$$b15
000168727 7001_ $$aBeaulieu, Yanick$$b16
000168727 7001_ $$aLevy, Jeff$$b17
000168727 7001_ $$aLaplante, Guy$$b18
000168727 7001_ $$aRamadorai, Arvind$$b19
000168727 7001_ $$aJarc, Anthony$$b20
000168727 7001_ $$aAndrews, Ben$$b21
000168727 7001_ $$aGarcia, Pablo$$b22
000168727 7001_ $$aNeemuchwala, Huzefa$$b23
000168727 7001_ $$aAndrusaite, Alina$$b24
000168727 7001_ $$aKimpe, Tom$$b25
000168727 7001_ $$aHawkes, David$$b26
000168727 7001_ $$aKelly, John D$$b27
000168727 7001_ $$aStoyanov, Danail$$b28
000168727 773__ $$0PERI:(DE-600)2861750-2$$a10.1016/j.euf.2021.04.006$$gp. S2405456921001127$$n2$$p613-622$$tEuropean urology focus$$v8$$x2405-4569$$y2022
000168727 909CO $$ooai:inrepo02.dkfz.de:168727$$pVDB
000168727 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)26a1176cd8450660333a012075050072$$aDeutsches Krebsforschungszentrum$$b8$$kDKFZ
000168727 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)523be43f52702f8ab3e8b2c8166c87fa$$aDeutsches Krebsforschungszentrum$$b9$$kDKFZ
000168727 9131_ $$0G:(DE-HGF)POF4-315$$1G:(DE-HGF)POF4-310$$2G:(DE-HGF)POF4-300$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$aDE-HGF$$bGesundheit$$lKrebsforschung$$vBildgebung und Radioonkologie$$x0
000168727 9130_ $$0G:(DE-HGF)POF3-315$$1G:(DE-HGF)POF3-310$$2G:(DE-HGF)POF3-300$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bGesundheit$$lKrebsforschung$$vImaging and radiooncology$$x0
000168727 9141_ $$y2021
000168727 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2020-09-09
000168727 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2020-09-09
000168727 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2022-11-11
000168727 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2022-11-11
000168727 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2022-11-11
000168727 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2022-11-11
000168727 915__ $$0StatID:(DE-HGF)1110$$2StatID$$aDBCoverage$$bCurrent Contents - Clinical Medicine$$d2022-11-11
000168727 9201_ $$0I:(DE-He78)E130-20160331$$kE130$$lE130 Intelligente Medizinische Systeme$$x0
000168727 980__ $$ajournal
000168727 980__ $$aVDB
000168727 980__ $$aI:(DE-He78)E130-20160331
000168727 980__ $$aUNRESTRICTED