Ethical Decision-Making in Autonomous Surgical Robotics
Keywords:
autonomous surgical robotics, artificial intelligence, surgical ethics, human oversight, patient safety, medical robotics, responsible AIAbstract
Increasing autonomy in surgical robotics is changing the relationship between artificial intelligence, surgeons, patients, and medical-device manufacturers. Contemporary research has progressed from robot-assisted teleoperation toward autonomous execution of defined surgical tasks, including soft-tissue manipulation, suturing, and experimental multistep procedures.
These developments create substantial opportunities for procedural consistency and precision but also raise ethical questions concerning patient safety, informed consent, accountability, explainability, human control, algorithmic uncertainty, cybersecurity, and responsibility when machine-generated actions contribute to harm. This study develops an ethical decision-making framework for autonomous surgical robotics and evaluates its operation through a reproducible simulation of 1,200 surgical decision episodes across four conditions: standardized anatomy, unexpected bleeding, tissue deformation, and instrument occlusion. Correct autonomous decision rates ranged from 84.3% to 96.7%, while simulated unresolved unsafe-action rates increased substantially under unexpected intraoperative conditions. Introducing confidence thresholds, automated safety pauses, surgeon escalation, and bounded execution reduced the overall unsafe-action rate from 6.9% to 2.3%, representing an approximate 67.5% relative reduction.
The analysis demonstrates that autonomy should not be interpreted as unrestricted machine independence. Instead, ethically acceptable autonomy requires risk-sensitive escalation, meaningful human control, transparent responsibility allocation, continuous monitoring, robust cybersecurity, and explicit fail-safe mechanisms. The proposed Ethical Surgical
Autonomy Decision Framework integrates technical performance with beneficence, non-maleficence, patient autonomy, accountability, traceability, proportionality, and post-market learning to support safer development of increasingly autonomous surgical systems.
