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@ARTICLE{Collins:168727,
      author       = {J. W. Collins and H. J. Marcus and A. Ghazi and A. Sridhar
                      and D. Hashimoto and G. Hager and A. Arezzo and P. Jannin
                      and L. Maier-Hein$^*$ and K. Marz$^*$ and P. Valdastri and
                      K. Mori and D. Elson and S. Giannarou and M. Slack and L.
                      Hares and Y. Beaulieu and J. Levy and G. Laplante and A.
                      Ramadorai and A. Jarc and B. Andrews and P. Garcia and H.
                      Neemuchwala and A. Andrusaite and T. Kimpe and D. Hawkes and
                      J. D. Kelly and D. Stoyanov},
      title        = {{E}thical implications of {AI} in robotic surgical
                      training: {A} {D}elphi consensus statement.},
      journal      = {European urology focus},
      volume       = {8},
      number       = {2},
      issn         = {2405-4569},
      address      = {Amsterdam},
      publisher    = {Elsevier},
      reportid     = {DKFZ-2021-01031},
      pages        = {613-622},
      year         = {2022},
      note         = {2022 Mar;8(2):613-622},
      abstract     = {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.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.},
      subtyp        = {Review Article},
      keywords     = {Artificial intelligence (Other) / Computer vision (Other) /
                      Deep learning (Other) / GDPR (Other) / Learning algorithms
                      (Other) / Natural language processing (Other) / biases
                      (Other) / curriculum development (Other) / data protection
                      (Other) / machine learning (Other) / narrow AI (Other) /
                      predictive analytics (Other) / privacy (Other) / risk
                      prediction (Other) / surgical education (Other) / training
                      (Other) / transparency (Other)},
      cin          = {E130},
      ddc          = {610},
      cid          = {I:(DE-He78)E130-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:33941503},
      doi          = {10.1016/j.euf.2021.04.006},
      url          = {https://inrepo02.dkfz.de/record/168727},
}