The advent of artificial intelligence (“AI”) holds great potential to improve clinical diagnostics. At the same time, there are important questions of liability for harms arising from the use of this technology. Due to their complexity, opacity, and lack of foreseeability, AI systems are not easily accommodated by traditional liability frameworks. This difficulty is compounded in the health care space where various actors, namely physicians and health care organizations, are subject to distinct but interrelated legal duties regarding the use of health technology. Without a principled way to apportion responsibility among these actors, patients may find it difficult to recover for injuries. In this Article, I propose that physicians, manufacturers of clinical AI systems, and hospitals be considered a common enterprise for the purposes of liability. This proposed framework helps facilitate the apportioning of responsibility among disparate actors under a single legal theory. Such an approach responds to concerns about the responsibility gap engendered by clinical AI technology as it shifts away from individualistic notions of responsibility, embodied by negligence and products liability, toward a more distributed conception. In addition to favoring plaintiff recovery, a common enterprise strict liability approach would create strong incentives for the relevant actors to take care.

Dennis Lindberg came into his aunt’s care when he was in the 4th grade because his parents struggled with drug addiction and could not provide for him. At thirteen, he was baptized in his aunt’s faith as a Jehovah’s Witness. Just days after he turned fourteen, on November 6, he was diagnosed with acute lymphoblastic leukemia.

The prognosis was that Dennis had a 75% chance of cure with standard oncology treatment. Consistent with the requirements of his new faith, however, Dennis told his doctors, “I do not want to be treated if the requirement is that I would have to take a blood transfusion.” His aunt, whose custodial rights seem not to have included medical decisionmaking, was adamant that “[t]his is Dennis’s decision.”

The hospital social worker assigned to Dennis’s case assured his aunt that “having just turned 14, [he] could be considered mature enough to make his own decisions.” But hospital psychologists declined to evaluate Dennis’s maturity because they did “not have the tools for such an assessment.” Established hospital policy described the failure to provide a minor with necessary, life-saving care as medical neglect. Hospital ethicists advised that Dennis’s autonomy interests were outweighed by the benefits associated with treatment. And, standard oncology practice norms are that doctors should push back against minors’ lifesaving treatment refusals. Still, Dennis’s doctor agreed with his social worker and aunt, saying, “We owe respect to a 14-year-old[.]” In this view, he was supported by colleagues on staff and by hospital counsel.

A county judge got the case only at the eleventh hour, on an emergency motion filed by Dennis’s parents and Child Protective Services for a declaration of dependency and to compel the necessary transfusion. Although Dennis was degrading rapidly, his doctor testified that if a transfusion were ordered that day, Dennis still had a 70% chance of survival. The judge had no background in the applicable law and no time to research the issues; nevertheless, he denied the motion concluding, “It is time to do what Dennis has decided.” “Seven hours later, at 6 p.m., on Nov. 28, Dennis died.”1

How can an agency like the U.S. Food & Drug Administration (“FDA”) effectively regulate software that is constantly learning and adapting to real-world data? Continuously learning algorithms pose significant public health risks if a medical device can change overtime to fundamentally alter the nature of a device post-market. This Article evaluates the FDA’s proposed regulatory framework for artificially intelligent medical devices against the backdrop of the current technology, as well as industry professionals’ desired trajectory, to determine whether the proposed regulatory framework can ensure safe and reliable medical devices without stifling innovation. Ultimately, the FDA succeeds in placing effective limits on continuously learning algorithms while giving manufacturers freedom to allow their devices to adapt to real-world data. The framework, however, does not give adequate attention to protecting patient data, monitoring cybersecurity, and ensuring safety and efficacy. The FDA, medical device industry, and relevant policymakers should increase oversight of these areas to protect patients and providers relying on this new technology.

The COVID-19 pandemic brought critical debates regarding private equity ownership of long-term care facilities to the forefront of political, legal, and social landscapes. Like many of the historical concerns about long-term care, these debates center around low quality patient care. While the concerns present important challenges to overcome, this note theorizes the kinds of organizational incentives that may provide opportunities to align patient quality care with the financial goals of private equity investing. After a discussion of the historical context of long term care facilities and the more recent trends towards for-profit and private equity ownership of these facilities (Parts II and III), I engage with value-based models as a starting point to consider organizational level incentive possibilities (Part IV). In Part V, I consider an organizational-level pay for performance model, a time-bound incentive structure, and investor-specific incentives as three distinct possibilities for addressing the patient care issues identified.

Individuals with disabilities are sixteen times more likely to be killed by officers during a law enforcement encounter than other individuals. As the ever-growing list of victims demonstrates, law enforcement violence against individuals with disabilities is a national crisis. Yet, the current test, developed under Graham v. Connor, for whether officers’ use of force is excessive during an arrest considers only three factors: severity of the crime, immediacy of the threat, and resistance to arrest or attempts to flee. On its face, Graham’s three-factor test does not contemplate whether an arrestee’s individual characteristics are relevant to an officer’s use of force. Recognizing that the Graham factors are “non-exhaustive” and “flexible,” some lower federal courts have relaxed the excessive force test to account for particular circumstances. However, there is no consensus among the circuit courts and the Supreme Court has not revisited the Graham test.

Over three decades later, courts still do not have sufficient guidance on how to address individual disability under Graham. This Note advocates that in adherence to Graham’s expressed flexibility, its three-factor test should be reformulated to add in a fourth factor inspired by Title II of the Americans with Disabilities Act to account for whether “reasonable modifications” of an individual’s disability were made in situations when law enforcement employs force during the course of an arrest. Applying this standard in cases where an officer “reasonably should know” the arrestee has a disability promotes a baseline assumption that law enforcement officers have an active role in accommodating all disabilities.