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

Autor: Justin W. Collins, Ben Andrews, Mark Slack, Arvind Ramadorai, Gregory D. Hager, Kensaku Mori, Ashwin Sridhar, Daniel A. Hashimoto, Yanick Beaulieu, Jeff Levy, Huzefa Neemuchwala, Pablo Garcia, John D. Kelly, Stamatia Giannarou, Guy Laplante, Pierre Jannin, Hani J. Marcus, Anthony M. Jarc, Alberto Arezzo, Daniel S. Elson, Pietro Valdastri, Tom Kimpe, Luke David Ronald Hares, Ahmed Ghazi, Alina Andrusaite, Lena Maier-Hein, Danail Stoyanov, Keno März, David J. Hawkes
Přispěvatelé: University College of London [London] (UCL), Johns Hopkins University (JHU), Università degli studi di Torino (UNITO), Laboratoire Traitement du Signal et de l'Image (LTSI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES), CHU Pontchaillou [Rennes], German Cancer Research Center - Deutsches Krebsforschungszentrum [Heidelberg] (DKFZ), University of Leeds, The Hamlyn Centre [London], Imperial College London, University of Cambridge [UK] (CAM), Université de Montréal (UdeM), Barco, The work was supported by the Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) UK [203145Z/16/Z], Engineering and Physical Sciences Research Council (EPSRC) UK [EP/P027938/1, EP/R004080/1, EP/P012841/1], The Royal Academy of Engineering Chair in Emerging Technologies scheme, UK., University of Rochester [USA], Massachusetts General Hospital [Boston], Università degli studi di Torino = University of Turin (UNITO), Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM), Nagoya University, Medtronic SNT, Medtronic, Johnson & Johnson Corporation, Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de la Santé et de la Recherche Médicale (INSERM)
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Artificial intelligence
Consensus
curriculum development
Delphi Technique
Urology
education
030232 urology & nephrology
Delphi method
surgical education
Context (language use)
Learning algorithms
privacy
[SDV.MHEP.UN]Life Sciences [q-bio]/Human health and pathology/Urology and Nephrology
03 medical and health sciences
risk prediction
0302 clinical medicine
Robotic Surgical Procedures
Health care
Data Protection Act 1998
Medicine
Humans
GDPR
Curriculum
computer.programming_language
transparency
Medical education
data protection
training
business.industry
Natural language processing
Reproducibility of Results
Deep learning
narrow AI
Predictive analytics
3. Good health
predictive analytics
machine learning
030220 oncology & carcinogenesis
biases
Computer vision
[SDV.IB]Life Sciences [q-bio]/Bioengineering
Applications of artificial intelligence
business
computer
Delphi
Zdroj: European Urology Focus
European Urology Focus, Elsevier, 2021, ⟨10.1016/j.euf.2021.04.006⟩
European Urology Focus, 2022, 8 (2), pp.613-622. ⟨10.1016/j.euf.2021.04.006⟩
European Urology Focus, Elsevier, 2022, 8 (2), pp.613-622. ⟨10.1016/j.euf.2021.04.006⟩
ISSN: 2405-4569
DOI: 10.1016/j.euf.2021.04.006⟩
Popis: International audience; 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.
Databáze: OpenAIRE
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