Ethical implications of AI in robotic surgical training: A Delphi consensus statement
This consensus statement provides ethical and practical guidance on developing and deploying narrow AI applications for robotic surgical training curricula. Utilizing a three-round Delphi process with an international panel of 30 clinical, academic, and industry experts, the statement addresses key areas including data protection and privacy, reproducibility and transparency, predictive analytics, inherent biases, and accountability. The guidance aims to establish standardized approaches to utilizing surgical data while ensuring ethical deployment and patient safety.
Summary and content fields are drawn from the document text and are pending editorial review.
Who issued it
Who it governs
Researchers, clinicians, developers, and industry stakeholders involved in designing or deploying AI systems for surgical training should read this to understand ethical guidelines and data standards.
What the document contains
The document addresses ethical and regulatory concerns surrounding the use of artificial intelligence and machine learning in robotic surgical training. It covers data protection, privacy, transparency, reproducibility, inherent biases, accountability, and the practical application of AI algorithms to surgical education and curricula.
The document does not address specific considerations required for deep learning algorithms versus classical rule-based algorithms, nor does it provide binding legal enforcement mechanisms.
- An international expert panel achieved consensus using a Delphi methodology to formulate ethical guidance for narrow AI applications in robotic surgical training.
- There is a recognized lack of existing guidelines governing the ethical utilization of AI in surgical training environments.
- Standardized data collection and labeling, particularly combining automated and manual approaches, are essential for optimizing machine learning datasets and patient safety.
- While AI can mitigate certain human confirmation and interpretation biases, careful consideration must be given to avoid exacerbating prediction and information biases.
- Accountability structures, explicit patient and surgeon consent, and transparent data protection measures under frameworks like GDPR are mandatory before clinical deployment.
· Abstract
· Introduction
· Materials and methods
- Review of the literature
- Expert panel conference meeting
- Internet survey and Delphi process
· Results
- Formulation of guidance
- Section 1: Terminology
- Section 2: Data and privacy issues
- Section 3: Transparency and reproducibility
- Section 4: Biases
- Section 5: Accountability and liability
- Section 6: Application of AI algorithms in robotic surgical training
· Discussion
· Limitations
· ConclusionsHow it scores
Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.
Fairness
3/5Non-discrimination and equitable treatment
Transparency also has implications for biases, as AI technologies have the potential for algorithmic bias, reinforcing discriminatory practices based on data points such as position, experience or race, sex, or other features
Section 4
screening score · 2026-10-23
Human Dignity
1/5Respect for the inherent worth of persons
Do you agree that as clinicians we have responsibility to our patients to improve their care. Therefore, systems need to be developed to protect privacy, whilst allowing AI advancement to improve care
Appendix 2
screening score · 2026-10-23
Human Oversight
2/5Humans remain in control of consequential decisions
At the core of robotic surgery technology are data processing and control algorithms that translate the surgeon's hand, wrist, and finger movements into precise movements of miniaturised surgical instruments
Section 1
screening score · 2026-10-23