Ethical implications of AI in robotic surgical training: A Delphi consensus statement

Justin W Collins (1) , Hani J Marcus (1) , Ahmed Ghazi (2) , Ashwin Sridhar (1) , Daniel Hashimoto (3) , Gregory Hager (4) , Alberto Arezzo (5) , Pierre Jannin (6, 7) , Lena Maier-Hein (8) , Keno Marz (8) , Pietro Valdastri (9) , Kensaku Mori (10) , Daniel Elson (11) , Stamatia Giannarou (11) , Mark Slack (12) , Luke Hares , Yanick Beaulieu (13) , Jeff Levy , Guy Laplante (14) , Arvind Ramadorai (14) , Anthony Jarc , Ben Andrews , Pablo Garcia (15) , Huzefa Neemuchwala (15) , Alina Andrusaite (15) , Tom Kimpe (16) , David Hawkes (1) , John D Kelly (1) , Danail Stoyanov (1)

1 UCL - University College of London [London]
2 University of Rochester [USA]
3 Massachusetts General Hospital [Boston]
4 JHU - Johns Hopkins University
5 UNITO - Università degli studi di Torino = University of Turin
6 LTSI - Laboratoire Traitement du Signal et de l'Image
7 Centre Hospitalier Universitaire de Rennes [CHU Rennes] = Rennes University Hospital [Ponchaillou]
8 DKFZ - German Cancer Research Center - Deutsches Krebsforschungszentrum [Heidelberg]
9 University of Leeds
10 Nagoya University
11 The Hamlyn Centre [London]
12 CAM - University of Cambridge [UK]
13 UdeM - Université de Montréal
14 Medtronic SNT
15 Johnson & Johnson Corporation
16 Barco

CONTEXT: 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. OBJECTIVES: 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. EVIDENCE ACQUISITION: 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. EVIDENCE SYNTHESIS: 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. CONCLUSIONS: 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. PATIENT SUMMARY: 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.

Mots clés: Artificial intelligence; Computer vision; Deep learning; GDPR; Learning algorithms; Natural language processing; biases; curriculum development; data protection; machine learning; narrow AI; predictive analytics; privacy; risk prediction; surgical education; training; transparency

Domaines: Ingénierie biomédicale; Urologie et Néphrologie

Justin W Collins, Hani J Marcus, Ahmed Ghazi, Ashwin Sridhar, Daniel Hashimoto, et al.. Ethical implications of AI in robotic surgical training: A Delphi consensus statement. European Urology Focus, 2022, 8 (2), pp.613-622. ⟨10.1016/j.euf.2021.04.006⟩. ⟨hal-03268636⟩