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 …

Human-Centered Design To Address Biases In Artificial Intelligence, Ellen W. Clayton, You Chen, Laurie L. Novak, Shilo Anders, Bradley Malin

Vanderbilt Law School Faculty Publications

The potential of artificial intelligence (AI) to reduce health care disparities and inequities is recognized, but it can also exacerbate these issues if not implemented in an equitable manner. This perspective identifies potential biases in each stage of the AI life cycle, including data collection, annotation, machine learning model development, evaluation, deployment, operationalization, monitoring, and feedback integration. To mitigate these biases, we suggest involving a diverse group of stakeholders, using human-centered AI principles. Human-centered AI can help ensure that AI systems are designed and used in a way that benefits patients and society, which can reduce health disparities and inequities. …

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 …

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 …

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.

Regulating New Tech: Problems, Pathways, And People, Cary Coglianese

All Faculty Scholarship

New technologies bring with them many promises, but also a series of new problems. Even though these problems are new, they are not unlike the types of problems that regulators have long addressed in other contexts. The lessons from regulation in the past can thus guide regulatory efforts today. Regulators must focus on understanding the problems they seek to address and the causal pathways that lead to these problems. Then they must undertake efforts to shape the behavior of those in industry so that private sector managers focus on their technologies’ problems and take actions to interrupt the causal pathways. …

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

Articles

Problematic Interactions Between AI and Health Privacy Nicholson Price, University of Michigan Law SchoolFollow Abstract The interaction of artificial intelligence (AI) and health privacy is a two-way street. Both directions are problematic. This Essay 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 …

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 …

New Innovation Models In Medical Ai, Nicholson Price Ii, Rachel Sachs, Rebecca S. Eisenberg

Law & Economics Working Papers

In recent years, scientists and researchers have devoted considerable resources to developing medical artificial intelligence (AI) technologies. Many of these technologies—particularly those which 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 that relate 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 …

Ai's Legitimate Interest: Towards A Public Benefit Privacy Model, Charlotte A. Tschider

Faculty Publications & Other Works

Health data uses are on the rise. Increasingly more often, data are used for a variety of operational, diagnostic, and technical uses, as in the Internet of Health Things. Never has quality data been more necessary: large data stores now power the most advanced artificial intelligence applications, applications that may enable early diagnosis of chronic diseases and enable personalized medical treatment. These data, both personally identifiable and de-identified, have the potential to dramatically improve the quality, effectiveness, and safety of artificial intelligence.

Existing privacy laws do not 1) effectively protect the privacy interests of individuals and 2) provide the flexibility …

Busting Myths And Dispelling Doubts About Covid-19, Mark Findlay

Research Collection Yong Pung How School Of Law

The Centre for AI and Data Governance (CAIDG) at Singapore Management University (SMU) has embarked over past months on a programme of research designed to confront concerns about the pandemic and its control. Our interest is primarily directed to the ways in which AI-assisted technologies and mass data sharing have become a feature of pandemic control strategies. We want to know what impact these developments are having on community confidence and health safety. In developing this work, we have come across many myths that need busting.

The Healthcare Privacy-Artificial Intelligence Impasse, Charlotte A. Tschider

Faculty Publications & Other Works

With the advent of the Internet, wireless technologies, advanced computing, and, ultimately, the integration of mobile devices into patient care, medical device technologies have revolutionized the healthcare sector. What once was a highly personal, one-to-one relationship between physician and patient has now been expanded, including medical device manufacturers, third party healthcare system providers, even physician-as-a-service for interpreting the data complex systems churn out. The introduction of technology to the healthcare field has, at an ever-increasing rate, transformed human health management.

Reworking privacy commitments in an AI world is an important endeavor. It may mean that we reconceptualize what these rights …

Frontiers In Precision Medicine Iv: Artificial Intelligence, Assembling Large Cohorts, And The Population Data Revolution, Adam Bress, Rich Albrechtsen, Monika Baker, Jorge L. Contreras, Zachary Fica, Austin Gamblin, Chelsea Ratcliff, Bianca E. Rich, Matt A. Szaniawski, Alyssa Thorman, Chad Vansant-Webb, Willard Dere

Utah Law Faculty Scholarship

Large cohort studies and more recently electronic medical records (EMR) are being used to collect massive amounts of genetic information. Implementation of artificial intelligence has become increasingly necessary to interpret this data with the goal of augmenting patient care. While it is impossible to predict what the future holds, policy makers are challenged to create guiding principles and responsibly roll out these new technologies. On March 22, 2019, the University of Utah hosted its fourth annual Precision Medicine Symposium focusing on artificial intelligence, assembling large cohorts, and the population data revolution. The symposium brought together experts in medicine, science, law …

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 …

What Genetic Testing Teaches About Long-Term Predictive Health Analytics Regulation, Sharona Hoffman

Faculty Publications

The ever-growing phenomenon of predictive health analytics is generating significant excitement, hope for improved health outcomes, and potential for new revenues. Researchers are developing algorithms to predict suicide, heart disease, stroke, diabetes, cognitive decline, future opioid abuse, and other ailments. The researchers include not only medical experts, but also commercial enterprises such as Facebook and LexisNexis, who may profit from the work considerably. This Article focuses on long-term disease predictions (predictions regarding future illnesses), which have received surprisingly little attention in the legal and ethical literature. It compares the robust academic and policy debates and legal interventions that followed the …

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 …