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Updated: Nov 7, 2025

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

Justin W Collins1, Hani J Marcus2, Ahmed Ghazi3

  • 1University College London, Division of Surgery and Interventional Science, Research Department of Targeted Intervention; Wellcome/ESPRC Centre for Interventional and Surgical Sciences (WEISS), University College London; University College London Hospital, Division of Uro-oncology.

European Urology Focus
|May 4, 2021
PubMed
Summary

Ethical guidance for artificial intelligence (AI) in surgical training was developed using expert consensus. This framework addresses data privacy, bias, and transparency in AI applications for surgical education.

Keywords:
Artificial intelligenceComputer visionDeep learningGDPRLearning algorithmsNatural language processingbiasescurriculum developmentdata protectionmachine learningnarrow AIpredictive analyticsprivacyrisk predictionsurgical educationtrainingtransparency

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Area of Science:

  • Medical Education
  • Artificial Intelligence
  • Surgical Training

Background:

  • The expanding role of artificial intelligence (AI) in healthcare necessitates ethical guidelines.
  • Awareness of potential AI pitfalls requires structured guidance for safe implementation.

Purpose of the Study:

  • To provide ethical guidance for developing narrow AI applications in surgical training curricula.
  • To establish standardized approaches for AI-driven surgical training, addressing ethical implications of AI on surgical data.

Main Methods:

  • A 3-phase project involving a steering group, an expert panel, and an online Delphi process.
  • Literature review, expert discussion, survey creation, and 3 rounds of Delphi consensus among 30 experts.
  • Consensus defined as ≥80% agreement, with guidance formulation based on expert input.

Main Results:

  • 100% consensus on the lack of current guidance for AI in robotic surgical training.
  • Achieved consensus on critical areas: data protection, privacy, reproducibility, transparency, predictive analytics, inherent biases, and optimal training areas for AI.
  • Guidance demonstrated good internal consistency (Cronbach alpha >0.8).

Conclusions:

  • International expert consensus was achieved via Delphi methodology for ethical AI in surgical training.
  • Developed content-validated guidance provides an ethical foundation for narrow AI applications in surgical training.
  • Further validation of the developed guidance is recommended.