ArticleFrontiers in surgery2022
Autonomous surgical robotic systems and the liability dilemma.
Article in Frontiers in surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Medical liability in paediatric robotic surgery - a current concept and a literature review.Journal of robotic surgery · 2026Review
- Bibliometric and knowledge-map analysis of research on robot-assisted vascular interventional surgery (2015-2025).Journal of robotic surgery · 2026Article
- Applications of Reinforcement Learning for Autonomous Surgical Robotics: A Systematic Review.Biomimetics (Basel, Switzerland) · 2026Review
- Liability in Telesurgery: Navigating Legal, Ethical, and Cross-Border Challenges in the Era of Global Surgical Care.Medical principles and practice : international journal of the Kuwait University, Health Science Centre · 2026Review
- The Transformative Role of Artificial Intelligence in Plastic and Reconstructive Surgery: Challenges and Opportunities.Journal of clinical medicine · 2025Review
- Robotic versus Mini-Laparoscopic Colposacropexy to Treat Pelvic Organ Prolapse: A Retrospective Observational Cohort Study and a Medicolegal Perspective.Journal of clinical medicine · 2024Article
- A surgical activity model of laparoscopic cholecystectomy for co-operation with collaborative robots.Surgical endoscopy · 2024Article
- The IDEAL framework for surgical robotics: development, comparative evaluation and long-term monitoring.Nature medicine · 2024Review
- The Emperor's new clothes.Surgery open science · 2024Article
- Surgical Frontiers: A Comparative Review of Robotics Versus Laparoscopy in Gynecological Interventions.Cureus · 2023Review
- In the Hands of a Robot, From the Operating Room to the Courtroom: The Medicolegal Considerations of Robotic Surgery.Cureus · 2023Review
- The role of artificial intelligence in surgical simulation.Frontiers in medical technology · 2022Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Background: Advances in machine learning and robotics have allowed the development of increasingly autonomous robotic systems which are able to make decisions and learn from experience. This distribution of decision-making away from human supervision poses a legal challenge for determining liability. Methods: The iRobotSurgeon survey aimed to explore public opinion towards the issue of liability with robotic surgical systems. The survey included five hypothetical scenarios where a patient comes to harm and the respondent needs to determine who they believe is most responsible: the surgeon, the robot manufacturer, the hospital, or another party. Results: A total of 2,191 completed surveys were gathered evaluating 10,955 individual scenario responses from 78 countries spanning 6 continents. The survey demonstrated a pattern in which participants were sensitive to shifts from fully surgeon-controlled scenarios to scenarios in which robotic systems played a larger role in decision-making such that surgeons were blamed less. However, there was a limit to this shift with human surgeons still being ascribed blame in scenarios of autonomous robotic systems where humans had no role in decision-making. Importantly, there was no clear consensus among respondents where to allocate blame in the case of harm occurring from a fully autonomous system. Conclusions: The iRobotSurgeon Survey demonstrated a dilemma among respondents on who to blame when harm is caused by a fully autonomous surgical robotic system. Importantly, it also showed that the surgeon is ascribed blame even when they have had no role in decision-making which adds weight to concerns that human operators could act as "moral crumple zones" and bear the brunt of legal responsibility when a complex autonomous system causes harm.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.