Robotic Surgery and The Law: Defining Control and Criminal Responsibility

Abstract

The technological trend towards integration of robots in surgery is one of the breakthroughs in precision, consistency, and patient outcomes. The use of technologies, such as the da Vinci surgical system, has become a necessity in any operating room, because they allow to conduct minimally invasive surgery yet with more control and less human mistakes are made. But, with such systems, they begin to be much more autonomous, which brings on serious legal and ethical issues, especially concerning who bears the criminal liability and/or civil liability of the failure of the surgery or harm caused to the patient. The existing models of medical negligence and liability that assume intentional human action are ill prepared to handle the legal intricacies of intelligent semi-autonomous entities with human supervision. These issues get further complicated by overlapping of roles of stakeholders such as surgeon, Healthcare institutions, and technology manufacturers. The purpose of this paper is to discuss the legal ambiguity regarding robotic-assisted surgery and highlight how legal liability is rewarded when there is procedural error. Based on a doctrinal approach to the law study using the foreign case law as point of reference, this paper will examine the interpretation of culpability in healthcare through the prism of technology by various systems of law. The study suggests major flaws in the accountability, such as creating a definition of liability when the malfunctions of the machine or a mistake in an algorithm lead to an error. This paper presents the proposal on the model of distributed responsibility that takes into account the causation of Artificial Intelligence (AI) agents in robotic surgeries based on the analysis of expert interviews and case law. The model suggests the balancing of the distribution of liability between human and non-human actors to improve the brightening of law and patient safety. Based on the numerical results, machine learning (ML)-optimized hybrid (Model D) bests the rest by increasing legal accuracy, fairness, and flexibility of robot surgery liability. Finally, the study is calling on the policymakers and legal institutions to change the current legal doctrines to be in step with the highly developing surgical technologies.