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Full-Text Articles in Law
Locating Liability For Medical Ai, W. Nicholson Price Ii, I. Glenn Cohen
Locating Liability For Medical Ai, W. Nicholson Price Ii, I. Glenn Cohen
Articles
When medical AI systems fail, who should be responsible, and how? We argue that various features of medical AI complicate the application of existing tort doctrines and render them ineffective at creating incentives for the safe and effective use of medical AI. In addition to complexity and opacity, the problem of contextual bias, where medical AI systems vary substantially in performance from place to place, hampers traditional doctrines. We suggest instead the application of enterprise liability to hospitals—making them broadly liable for negligent injuries occurring within the hospital system—with an important caveat: hospitals must have access to the information needed …
New Innovation Models In Medical Ai, W Nicholson Price Ii, Rachel E. Sachs, Rebecca S. Eisenberg
New Innovation Models In Medical Ai, W Nicholson Price Ii, Rachel E. Sachs, Rebecca S. Eisenberg
Articles
In recent years, scientists and researchers have devoted considerable resources to developing medical artificial intelligence (AI) technologies. Many of these technologies—particularly those that resemble traditional medical devices in their functions—have received substantial attention in the legal and policy literature. But other types of novel AI technologies, such as those related to quality improvement and optimizing use of scarce facilities, have been largely absent from the discussion thus far. These AI innovations have the potential to shed light on important aspects of health innovation policy. First, these AI innovations interact less with the legal regimes that scholars traditionally conceive of as …
Exclusion Cycles: Reinforcing Disparities In Medicine, Ana Bracic, Shawneequa L. Callier, Nicholson Price
Exclusion Cycles: Reinforcing Disparities In Medicine, Ana Bracic, Shawneequa L. Callier, Nicholson Price
Articles
Minoritized populations face exclusion across contexts from politics to welfare to medicine. In medicine, exclusion manifests in substantial disparities in practice and in outcome. While these disparities arise from many sources, the interaction between institutions, dominant-group behaviors, and minoritized responses shape the overall pattern and are key to improving it. We apply the theory of exclusion cycles to medical practice, the collection of medical big data, and the development of artificial intelligence in medicine. These cycles are both self-reinforcing and other-reinforcing, leading to dismayingly persistent exclusion. The interactions between such cycles offer lessons and prescriptions for effective policy.
How Much Can Potential Jurors Tell Us About Liability For Medical Artificial Intelligence?, W. Nicholson Price Ii, Sara Gerke, I. Glenn Cohen
How Much Can Potential Jurors Tell Us About Liability For Medical Artificial Intelligence?, W. Nicholson Price Ii, Sara Gerke, I. Glenn Cohen
Articles
Artificial intelligence (AI) is rapidly entering medical practice, whether for risk prediction, diagnosis, or treatment recommendation. But a persistent question keeps arising: What happens when things go wrong? When patients are injured, and AI was involved, who will be liable and how? Liability is likely to influence the behavior of physicians who decide whether to follow AI advice, hospitals that implement AI tools for physician use, and developers who create those tools in the first place. If physicians are shielded from liability (typically medical malpractice liability) when they use AI tools, even if patient injury results, they are more likely …
Regulatory Responses To Medical Machine Learning, Timo Minssen, Sara Gerke, Mateo Aboy, W. Nicholson Price Ii, Glenn Cohen
Regulatory Responses To Medical Machine Learning, Timo Minssen, Sara Gerke, Mateo Aboy, W. Nicholson Price Ii, Glenn Cohen
Articles
Companies and healthcare providers are developing and implementing new applications of medical artificial intelligence, including the artificial intelligence sub-type of medical machine learning (MML).MML is based on the application of machine learning (ML) algorithms to automatically identify patterns and act on medical data to guide clinical decisions. MML poses challenges and raises important questions, including (1) How will regulators evaluate MML-based medical devices to ensure their safety and effectiveness? and (2) What additional MML considerations should be taken into account in the international context? To address these questions, we analyze the current regulatory approaches to MML in the USA and …
Potential Liability For Physicians Using Artificial Intelligence, W. Nicholson Price Ii, Sara Gerke, I Glenn Cohen
Potential Liability For Physicians Using Artificial Intelligence, W. Nicholson Price Ii, Sara Gerke, I Glenn Cohen
Articles
Artificial intelligence (AI) is quickly making inroads into medical practice, especially in forms that rely on machine learning, with a mix of hope and hype. Multiple AI-based products have now been approved or cleared by the US Food and Drug Administration (FDA), and health systems and hospitals are increasingly deploying AI-based systems. For example, medical AI can support clinical decisions, such as recommending drugs or dosages or interpreting radiological images.2 One key difference from most traditional clinical decision support software is that some medical AI may communicate results or recommendations to the care team without being able to communicate the …
Privacy In The Age Of Medical Big Data, W. Nicholson Price Ii, I. Glenn Cohen
Privacy In The Age Of Medical Big Data, W. Nicholson Price Ii, I. Glenn Cohen
Articles
Big data has become the ubiquitous watch word of medical innovation. The rapid development of machine-learning techniques and artificial intelligence in particular has promised to revolutionize medical practice from the allocation of resources to the diagnosis of complex diseases. But with big data comes big risks and challenges, among them significant questions about patient privacy. Here, we outline the legal and ethical challenges big data brings to patient privacy. We discuss, among other topics, how best to conceive of health privacy; the importance of equity, consent, and patient governance in data collection; discrimination in data uses; and how to handle …