Law Commons^™

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 …

Humans In The Loop, Nicholson Price Ii, Rebecca Crootof, Margot Kaminski

Articles

From lethal drones to cancer diagnostics, humans are increasingly working with complex and artificially intelligent algorithms to make decisions which affect human lives, raising questions about how best to regulate these “human in the loop” systems. We make four contributions to the discourse.

First, contrary to the popular narrative, law is already profoundly and often problematically involved in governing human-in-the-loop systems: it regularly affects whether humans are retained in or removed from the loop. Second, we identify “the MABA-MABA trap,” which occurs when policymakers attempt to address concerns about algorithmic incapacities by inserting a human into decision making process. Regardless …

Open-Source Clinical Machine Learning Models: Critical Appraisal Of Feasibility, Advantages, And Challenges, Keerthi B. Harish, W. Nicholson Price Ii, Yindalon Aphinyanaphongs

Articles

Machine learning applications promise to augment clinical capabilities and at least 64 models have already been approved by the US Food and Drug Administration. These tools are developed, shared, and used in an environment in which regulations and market forces remain immature. An important consideration when evaluating this environment is the introduction of open-source solutions in which innovations are freely shared; such solutions have long been a facet of digital culture. We discuss the feasibility and implications of open-source machine learning in a health care infrastructure built upon proprietary information. The decreased cost of development as compared to drugs and …

Volume Introduction, I. Glenn Cohen, Timo Minssen, W. Nicholson Price Ii, Christopher Robertson, Carmel Shachar

Other Publications

Medical devices have historically been less regulated than their drug and biologic counterparts. A benefit of this less demanding regulatory regime is facilitating innovation by making new devices available to consumers in a timely fashion. Nevertheless, there is increasing concern that this approach raises serious public health and safety concerns. The Institute of Medicine in 2011 published a critique of the American pathway allowing moderate-risk devices to be brought to the market through the less-rigorous 501(k) pathway,1 flagging a need for increased postmarket review and surveillance. High-profile recalls of medical devices, such as vaginal mesh products, along with reports globally …

Distributed Governance Of Medical Ai, W. Nicholson Price Ii

Law & Economics Working Papers

Artificial intelligence (AI) promises to bring substantial benefits to medicine. In addition to pushing the frontiers of what is humanly possible, like predicting kidney failure or sepsis before any human can notice, it can democratize expertise beyond the circle of highly specialized practitioners, like letting generalists diagnose diabetic degeneration of the retina. But AI doesn’t always work, and it doesn’t always work for everyone, and it doesn’t always work in every context. AI is likely to behave differently in well-resourced hospitals where it is developed than in poorly resourced frontline health environments where it might well make the biggest difference …

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 …

Part I - Ai And Data As Medical Devices, W. Nicholson Price Ii

Other Publications

It may seem counterintuitive to open a book on medical devices with chapters on software and data, but these are the frontiers of new medical device regulation and law. Physical devices are still crucial to medicine, but they – and medical practice as a whole – are embedded in and permeated by networks of software and caches of data. Those software systems are often mindbogglingly complex and largely inscrutable, involving artificial intelligence and machine learning. Ensuring that such software works effectively and safely remains a substantial challenge for regulators and policymakers. Each of the three chapters in this part examines …

Liability For Use Of Artificial Intelligence In Medicine, W. Nicholson Price, Sara Gerke, I. Glenn Cohen

Law & Economics Working Papers

While artificial intelligence has substantial potential to improve medical practice, errors will certainly occur, sometimes resulting in injury. Who will be liable? Questions of liability for AI-related injury raise not only immediate concerns for potentially liable parties, but also broader systemic questions about how AI will be developed and adopted. The landscape of liability is complex, involving health-care providers and institutions and the developers of AI systems. In this chapter, we consider these three principal loci of liability: individual health-care providers, focused on physicians; institutions, focused on hospitals; and developers.

Problematic Interactions Between Ai And Health Privacy, W. Nicholson Price Ii

Law & Economics Working Papers

The interaction of artificial intelligence (“AI”) and health privacy is a two-way street. Both directions are problematic. This Article makes two main points. First, the advent of artificial intelligence weakens the legal protections for health privacy by rendering deidentification less reliable and by inferring health information from unprotected data sources. Second, the legal rules that protect health privacy nonetheless detrimentally impact the development of AI used in the health system by introducing multiple sources of bias: collection and sharing of data by a small set of entities, the process of data collection while following privacy rules, and the use of …

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, 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 …

Outcome Prediction In The Practice Of Law, Mark K. Osbeck, Michael Gilliland

Articles

Business forecasters typically use time-series models to predict future demands, the forecasts informing management decision making and guiding organizational planning. But this type of forecasting is merely a subset of the broader field of predictive analytics, models used by data scientists in all manner of applications, including credit approvals, fraud detection, product-purchase and music-listening recommendations, and even the real-time decisions made by self-driving vehicles. The practice of law requires decisions that must be based on predictions of future legal outcomes, and data scientists are now developing forecasting methods to support the process. In this article, Mark Osbeck and Mike Gilliland …

Artificial Intelligence In Health Care: Applications And Legal Implications, W. Nicholson Price Ii

Articles

Artificial intelligence (AI) is rapidly moving to change the healthcare system. Driven by the juxtaposition of big data and powerful machine learning techniques—terms I will explain momentarily—innovators have begun to develop tools to improve the process of clinical care, to advance medical research, and to improve efficiency. These tools rely on algorithms, programs created from healthcare data that can make predictions or recommendations. However, the algorithms themselves are often too complex for their reasoning to be understood or even stated explicitly. Such algorithms may be best described as “black-box.” This article briefly describes the concept of AI in medicine, including …