Science and Technology 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 …

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 …

Ai Insurance: How Liability Insurance Can Drive The Responsible Adoption Of Artificial Intelligence In Health Care, Ariel Dora Stern, Avi Goldfarb, Timo Minssen, W. Nicholson Price Ii

Articles

Despite enthusiasm about the potential to apply artificial intelligence (AI) to medicine and health care delivery, adoption remains tepid, even for the most compelling technologies. In this article, the authors focus on one set of challenges to AI adoption: those related to liability. Well-designed AI liability insurance can mitigate predictable liability risks and uncertainties in a way that is aligned with the interests of health care’s main stakeholders, including patients, physicians, and health care organization leadership. A market for AI insurance will encourage the use of high-quality AI, because insurers will be most keen to underwrite those products that are …

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 …

Fostering Production Of Pharmaceutical Products In Developing Countries, William Fisher, Ruth L. Okediji, Padmashree Gehl Sampath

Michigan Journal of International Law

The ways in which pharmaceutical products are currently developed, manufactured, and distributed fail to meet the needs of developing countries. The recent emergence of new infectious diseases, the associated surge of healthcare nationalism, and the prevalence of substandard and falsified drugs have strengthened substantially the net benefits of augmenting the capacity of developing countries to produce such products locally. Most previous efforts to do so have foundered. The chance of success in the future would be maximized by the adoption of five strategies : (a) clarifying the zones of discretion created by the relevant treaties to ensure that local firms …

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.

Congressional Myopia In Biomedical Innovation Policy, W. Nicholson Price Ii

Reviews

Innovation policy is hard. Getting it right requires balancing incentives for developers, consumer access, rewards for later innovators, safety concerns, and other factors. This balance is vitally important and wickedly difficult—even when it’s the focus of concerted, careful, informed effort. How well should we expect it to go when innovation policy is made by accident? Enter The Accidental Innovation Policymakers, an illuminating new project by Professor Rachel Sachs. Sachs persuasively shows how Congress has repeatedly made substantial changes to innovation policy, seemingly without talking about, seriously considering, or even recognizing that it is doing so. There’s an asymmetry to this …

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.

Catch And Contain Novel Pathogens Early!—Assessing U.S. Medical Isolation Laws As Applied To A Future Pandemic Detection And Prevention Model, April Xiaoyi Xu

University of Michigan Journal of Law Reform Caveat

As of July 2, 2021, there have been 196,553,009 confirmed cases of the Coronavirus Disease (COVID-19), including 4,200,412 deaths, globally. Unfortunately, infectious diseases have been an “unavoidable fact of life” throughout history. While the global community looks forward to a gradual return to normalcy from COVID-19 with an increasing number of individuals getting vaccinated on a daily basis, the COVID-19 public health crisis has exposed significant inadequacies in many countries’ pandemic responses—the United States included. Governing authorities must actively consider more effective solutions to quickly detect and prevent the spread of future pandemics.

One proposed model that offers promising potential, …

Mitochondrial Replacement Therapy: Let The Science Decide, Sabrina K. Glavota

Michigan Technology Law Review

Mitochondrial replacement therapy (MRT) is an in vitro fertilization technique designed to prevent women who are carriers of mitochondrial diseases from passing on these heritable genetic diseases to their children. It is an innovative assisted reproductive technology that is only legal in a small number of countries. The United States has essentially stagnated all opportunities for research and clinical trials on MRT through a rider in H.R.2029 – Consolidated Appropriations Act, 2016. The rider bans clinical trials on all therapies in which a human embryo is intentionally altered to include a heritable genetic modification. This note argues that the rider …

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 …

Association For Molecular Pathology V. Myriad Genetics: A Critical Reassessment, Jorge L. Contreras

Michigan Technology Law Review

The Supreme Court’s 2013 decision in Association for Molecular Pathology v. Myriad Genetics is an essential piece of the Court’s recent quartet of patent eligibility decisions, which also includes Bilski v. Kappos, Mayo v. Prometheus, and Alice v. CLS Bank. Each of these decisions has significantly shaped the contours of patent eligibility under Section 101 of the Patent Act in ways that have been both applauded and criticized. The Myriad case, however, was significant beyond its impact on Section 101 jurisprudence. It was seen, and litigated, as a case impacting patient rights, access to healthcare, scientific freedom, …

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

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

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 …

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 …

Suggestions For State Laws On Biosimilar Substitution, Gary M. Fox

Michigan Telecommunications & Technology Law Review

Biologic drugs offer major advancements over small-molecule drugs when it comes to treating serious diseases. Biosimilars, which mimic innovative biologic drugs, have the potential to further revolutionize the practice of medicine. States now have decades of experience regulating the substitution of generic, small-molecule drugs for their brand-name equivalents. But the complexities of biologic drugs and biosimilars force states to confront novel scientific and legal issues. Many states have begun tackling those issues by passing laws that regulate when pharmacists may substitute biosimilars for their corresponding biologic drugs. Other states have yet to do so. This Note surveys five provisions common …

Risk And Resilience In Health Data Infrastructure, W. Nicholson Price Ii

Articles

Today’s health system runs on data. However, for a system that generates and requires so much data, the health care system is surprisingly bad at maintaining, connecting, and using those data. In the easy cases of coordinated care and stationary patients, the system works—sometimes. But when care is fragmented, fragmented data often result. Fragmented data create risks both to individual patients and to the system. For patients, fragmentation creates risks in care based on incomplete or incorrect information, and may also lead to privacy risks from a patched together system. For the system, data fragmentation hinders efforts to improve efficiency …

Regulating Black-Box Medicine, W. Nicholson Price Ii

Michigan Law Review

Data drive modern medicine. And our tools to analyze those data are growing ever more powerful. As health data are collected in greater and greater amounts, sophisticated algorithms based on those data can drive medical innovation, improve the process of care, and increase efficiency. Those algorithms, however, vary widely in quality. Some are accurate and powerful, while others may be riddled with errors or based on faulty science. When an opaque algorithm recommends an insulin dose to a diabetic patient, how do we know that dose is correct? Patients, providers, and insurers face substantial difficulties in identifying high-quality algorithms; they …

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 …

A Comment On Privacy And Accountability In Black-Box Medicine, Carl E. Schneider

Michigan Telecommunications & Technology Law Review

Human institutions and activities cannot avoid failures. Anxiety about them often provokes governments to try to prevent those failures. When that anxiety is vivid and urgent, government may do so without carefully asking whether regulation’s costs justify their benefits. Privacy and Accountability in Black Box Medicine admirably labors to bring discipline and rationality to thinking about an important development — the rise of “black-box medicine” — before it causes injuries regulation should have prevented and before it is impaired by improvident regulation. That is, Privacy and Accountability weighs the costs against the benefits of various forms of regulation across the …

R-Egg-Ulation: A Call For Greater Regulation Of The Big Business Of Human Egg Harvesting, Danielle A. Vera

Michigan Journal of Gender & Law

When it comes to young healthy women “donating” their eggs, America has a regulation problem. This Note explains the science behind the harvesting of human eggs, focusing on potential egg donors, and describes the specific factors that make egg donation a unique type of transaction. It describes the current regulatory status of the assisted reproductive technology industry in the United States and highlights the ways in which this scheme fails to protect egg “donors.” This Note concludes with a call for comprehensive regulation of the assisted reproductive technology industry.

Privacy And Accountability In Black-Box Medicine, Roger Allan Ford, W. Nicholson Price Ii

Michigan Telecommunications & Technology Law Review

Black-box medicine—the use of big data and sophisticated machine-learning techniques for health-care applications—could be the future of personalized medicine. Black-box medicine promises to make it easier to diagnose rare diseases and conditions, identify the most promising treatments, and allocate scarce resources among different patients. But to succeed, it must overcome two separate, but related, problems: patient privacy and algorithmic accountability. Privacy is a problem because researchers need access to huge amounts of patient health information to generate useful medical predictions. And accountability is a problem because black-box algorithms must be verified by outsiders to ensure they are accurate and unbiased, …

Diagnostics Need Not Apply, Rebecca S. Eisenberg

Articles

Diagnostic testing helps caregivers and patients understand a patient's condition, predict future outcomes, select appropriate treatments, and determine whether treatment is working. Improvements in diagnostic testing are essential to bringing about the long-heralded promise of personalized medicine. Yet it seems increasingly clear that most important advances in this type of medical technology lie outside the boundaries of patent-eligible subject matter. The clarity of this conclusion has been obscured by ambiguity in the recent decisions of the Supreme Court concerning patent eligibility. Since its 2010 decision in Bilski v. Kappos, the Court has followed a discipline of limiting judicial exclusions from …

Can You Diagnose Me Now? A Proposal To Modify The Fda’S Regulation Of Smartphone Mobile Health Applications With A Pre-Market Notification And Application Database Program, Stephen Mcinerney

University of Michigan Journal of Law Reform

Advances in mobile technology continually create new possibilities for the future of medical care. Yet these changes have also created concerns about patient safety. Under the Food, Drug, and Cosmetic Act, the Food and Drug Administration (FDA) has the authority to regulate a broad spectrum of products beyond traditional medical devices like stethoscopes or pacemakers. The regulatory question is not if the FDA has the statutory authority to regulate health-related software, but rather how it will exercise its regulatory authority. In September 2013, the FDA published Final Guidance on Mobile Medical Applications; in it, the Agency limited its oversight to …