Physicians are entrusted as the sole authorities to assess the competence of fellow physicians, and peer review is the primary pillar in assuring medical quality. A new era of intelligent tools may serve as the death knell for this insular physician-led, self-regulatory process. Increasingly, medical decision-making relies on advanced decision support with AI, and algorithms are asked to triage and prioritize patient care semi-autonomously. These technologies often lie beyond physician expertise, limiting the ability of any review process to determine where fault lies. Since machine learning may identify data patterns that guide treatments where there is current clinical equipoise, the best course of treatment may be swayed by information a physician may not have considered prima facie relevant, or by information that lies beyond published literature. Peer review committees determining if an error occurred will have less information than the machine that directed treatment. Our chapter discusses these challenges; explores implications that intelligent tools have on the organization of medicine and its structures of authority; considers the need for maintaining physician self-regulation; alternative legal approaches to maintaining quality assurance; and proposes new approaches to peer review and quality assurance for an era of medicine that utilizes these smart machines.

In the United States, a patchwork of privacy and antidiscrimination laws protects the rights of people with disabilities: the Health Information Portability and Accountability Act (HIPAA) protects disability-related patient data as it flows from one healthcare provider to another. If the information leaks out of the healthcare system, laws such as the Americans with Disabilities Act (ADA) and the Fair Housing Act (FHA) can contain it and mitigate its effects. This patchwork of laws often succeeds in preventing disability discrimination. However, emerging technologies such as artificial intelligence (AI) are rendering it ineffective.