Ethical, Legal, and Financial Considerations of Artificial Intelligence in Surgery

Abstract

Machine learning systems have become integrated into some of the most vital decision-making aspects of humanity, including hiring decisions, loan applications, and automobile safety, to name just a few. As applications increase in both gravity and complexity, the data quality and algorithmic interpretability of the systems must rise to meet those challenges. This is especially vital for navigating the nuances of health care, particularly among the high stakes of surgical operations. In addition to inherent ethical challenges of enabling a “black box” system to influence decision-making in patient care, the creation of biased datasets leads to biased algorithms with the power to perpetuate discrimination and reinforce disparities. Transparency and responsibility are paramount to the implementation of artificial intelligence in surgical decision-making and autonomous robotic surgery. Machine learning has been permeating health care across diverse clinical and surgical contexts but continues to face sizable obstacles, including apprehension from patients and providers alike. To integrate the technology fully while upholding standard of care and patient-provider trust, one must acknowledge and address the ethical, financial, and legal implications of using artificial intelligence for patient care.

Artificial Intelligence in Facial Plastic Surgery: A Review of Current Applications, Future Applications, and Ethical Considerations

“…Though this data was analyzed in a retrospective setting, studies like this will be used to inform development of real-time decisionmaking tools to aid surgeons in determining the most appropriate disposition for postoperative management. As described by Morris et al 40 in the event of patient harm, there remains to be a discussion as to whom the liability falls upon: the surgeon overriding a ML-based prediction or the developer of the technology. Future considerations for implementation of such technology into patient care would necessitate continued shared decision-making and an informed consent process in which detailed risks and benefits to AI-augmented care should be discussed with patients.…”

Artificial Intelligence in Facial Plastic and Reconstructive Surgery: A Systematic Review

“…In the literature, three main ethical concerns have been described; the first one is related to the need for informed consent and data use agreement to allow developers to use the information of the patients to create models, the second one is about the quality of the data used for the training of the algorithms and the third one, is that as the scientific papers data should be representative of the population where it will be used 1,23 Another concern in machine learning is that people train a computer with specific data to solve a particular problem, and it gives the output. However, the process used to solve could not be transparent because of the complexity; this phenomenon is called the black box 23,24 .…”

“…Liability would fall on the surgeon or the tool developer, or would it be shared? 23 Different models of Artificial Intelligence have been used in plastic surgery to classify beauty objectively; this could lead to discrimination of gender, culture, and race if a nonrepresentative data set is used to train the algorithms. Leading to a loss of diversity in aesthetic surgery and the underrepresentation of minorities 3,23 .…”

Artificial intelligence (AI) in the management of colorectal cancer: on the horizon?

“…Despite these exciting developments, many limitations remain. Significant financial investment and creation of regulatory and legal frameworks will be required to integrate AI into existing healthcare systems 13 . Furthermore, AI is constrained by the quality and quantity of available data used to train them.…”

“…Significant financial investment and creation of regulatory and legal frameworks will be required to integrate AI into existing healthcare systems. 13 Furthermore, AI is constrained by the quality and quantity of available data used to train them. As the majority of relevant studies come from only a few geographical locations, AI models are susceptible to inherent biases related to differing patient cohorts.…”