Law Commons^™

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 …

Medical Ai And Contextual Bias, W. Nicholson Price Ii

Articles

Artificial intelligence will transform medicine. One particularly attractive possibility is the democratization of medical expertise. If black-box medical algorithms can be trained to match the performance of high-level human experts — to identify malignancies as well as trained radiologists, to diagnose diabetic retinopathy as well as board-certified ophthalmologists, or to recommend tumor-specific courses of treatment as well as top-ranked oncologists — then those algorithms could be deployed in medical settings where human experts are not available, and patients could benefit. But there is a problem with this vision. Privacy law, malpractice, insurance reimbursement, and FDA approval standards all encourage developers …

Artificial Intelligence In The Medical System: Four Roles For Potential Transformation, Will Nicholson Price Ii

Articles

Artificial intelligence (AI) looks to transform the practice of medicine. As academics and policymakers alike turn to legal questions, a threshold issue involves what role AI will play in the larger medical system. This Article argues that AI can play at least four distinct roles in the medical system, each potentially transformative: pushing the frontiers of medical knowledge to increase the limits of medical performance, democratizing medical expertise by making specialist skills more available to non-specialists, automating drudgery within the medical system, and allocating scarce medical resources. Each role raises its own challenges, and an understanding of the four roles …

Biobanks As Innovation Infrastructure For Translational Medicine, W. Nicholson Price Ii

Book Chapters

Biobanks represent an opportunity for the use of big data to drive translational medicine. Precision medicine demands data to shape treatments to individual patient characteristics; large datasets can also suggest new uses for old drugs or relationships between previously unlinked conditions. But these tasks can be stymied when data are siloed in different datasets, smaller biobanks, or completely proprietary private resources. This hampers not only analysis of the data themselves, but also efforts to translate data-based insights into actionable recommendations and to transfer the discovered technology into a commercialization pipeline. Cross-project technological innovation, development, and validation are all more difficult …

The Cost Of Novelty, W. Nicholson Price Ii

Law & Economics Working Papers

Patent law tries to spur the development of new, better, innovative technology. But it focuses much more on “new” than “better” — and it turns out that “new” carries real social costs. I argue that patent law promotes innovation that diverges from existing technology, either a little (what I call “differentiating innovation”) or a lot (“exploring innovation”), at the expense of innovation that tells us more about existing technology (“deepening innovation”). Patent law’s focus on newness is unsurprising, and fits within a well-told narrative of innovative diversity accompanied by market selection of the best technologies. Unfortunately, innovative diversity brings not …

Cannabis For Medical Use: Fda And Dea Regulation In The Hall Of Mirrors, Rebecca S. Eisenberg, Deborah B. Leiderman

Articles

A majority of Americans now live in states that purport to authorize medical use of cannabis, although federal law continues to prohibit both recreational and medical use. The current legal regime for cannabis is unstable and may be more effective at deterring research than it is at deterring medical use. Lack of data on medical cannabis products poses public health risks as well as policy and legal challenges. Modified regulatory approaches for other kinds of products provide alternative models for encouraging safety and effectiveness research and providing better information about cannabis products already in clinical use.

Artificial Intelligence In The Medical System: Four Roles For Potential Transformation, W. Nicholson Price Ii

Articles

Artificial intelligence (AI) looks to transform the practice of medicine. As academics and policymakers alike turn to legal questions, including how to ensure high-quality performance by medical AI, a threshold issue involves what role AI will play in the larger medical system. This Article argues that AI can play at least four distinct roles in the medical system, each potentially transformative: pushing the frontiers of medical knowledge to increase the limits of medical performance, democratizing medical expertise by making specialist skills more available to non-specialists, automating drudgery within the medical system, and allocating scarce medical resources. Each role raises its …

Health Care Ai: Law, Regulation, And Policy., W. Nicholson Price Ii

Book Chapters

As discussed in previous chapters, artificial intelligence (AI) has the potential to be involved in almost all aspects of the health care industry. The legal landscape for health care AI is complex; AI systems with different intended uses, audiences, and use environments face different requirements at state, federal, and international levels. A full accounting of these legal requirements, or of the policy questions involved, is far beyond the scope of this chapter. Additionally, the legal and regulatory framework for AI in health care continues to evolve, given the nascent stage of the industry.

In this chapter, we offer an overview …

Risks And Remedies For Artificial Intelligence In Healthcare, W. Nicholson Price Ii

Other Publications

Artificial intelligence (AI) is rapidly entering health care and serving major roles, from automating drudgery and routine tasks in medical practice to managing patients and medical resources. As developers create AI systems to take on these tasks, several risks and challenges emerge, including the risk of injuries to patients from AI system errors, the risk to patient privacy of data acquisition and AI inference, and more. Potential solutions are complex but involve investment in infrastructure for high-quality, representative data; collaborative oversight by both the Food and Drug Administration and other health-care actors; and changes to medical education that will prepare …

Shadow Health Records Meet New Data Privacy Laws, W. Nicholson Price Ii, Margot E. Kaminski, Timo Minssen, Kayte Spector-Bagdady

Articles

Large sets of health data can enable innovation and quality measurement but can also create technical challenges and privacy risks. When entities such as health plans and health care providers handle personal health information, they are often subject to data privacy regulation. But amid a flood of new forms of health data, some third parties have figured out ways to avoid some data privacy laws, developing what we call “shadow health records”—collections of health data outside the health system that provide detailed pictures of individual health—that allow both innovative research and commercial targeting despite data privacy rules. Now that space …

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 …