Science and Technology Law Commons^™

Locating Liability For Medical Ai, W. Nicholson Price Ii, I. Glenn Cohen

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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

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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

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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 …

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

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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

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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

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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 …

Pandemic Surveillance Discrimination, Christian Sundquist

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The COVID-19 pandemic has laid bare the abiding tension between surveillance and privacy. Public health epidemiology has long utilized a variety of surveillance methods—such as contact tracing, quarantines, and mandatory reporting laws—to control the spread of disease during past epidemics and pandemics. Officials have typically justified the resulting intrusions on privacy as necessary for the greater public good by helping to stave off larger health crisis. The nature and scope of public health surveillance in the battle against COVID-19, however, has significantly changed with the advent of new technologies. Digital surveillance tools, often embedded in wearable technology, have greatly increased …

How The Covid-19 Pandemic Has And Should Reshape The American Safety Net, Gabriel Scheffler, Andrew Hammond, Ariel Jurow Kleiman

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No abstract provided.

Regulatory Responses To Medical Machine Learning, Timo Minssen, Sara Gerke, Mateo Aboy, W. Nicholson Price Ii, Glenn Cohen

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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

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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

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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

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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

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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 …

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

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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 …

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

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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 …

Diagnostics Need Not Apply, Rebecca S. Eisenberg

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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 …

Encouraging Maternal Sacrifice: How Regulations Governing The Consumption Of Pharmaceuticals During Pregnancy Prioritize Fetal Safety Over Maternal Health And Autonomy, Greer Donley

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Pregnant women are routinely faced with the stressful decision of whether to consume needed medications during their pregnancies. Because the risks associated with pharmaceutical drug consumption during pregnancy are largely unknown, pregnant women both inadvertently consume dangerous medications and avoid needed drugs. Both outcomes are harmful to pregnant women and their fetuses. This unparalleled lack of drug safety information is a result of ill-conceived, paternalistic regulations in two areas of the law: regulations governing ethical research in human subjects and regulations that dictate the required labels on drugs. The former categorizes pregnant women as “vulnerable” and thus precludes them from …

A System Of Men And Not Of Laws: What Due Process Tells Us About The Deficiencies In Institutional Review Boards, Greer Donley

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Governmental regulation of human subjects research involves unique agency action. It delegates power to non-expert committees, Institutional Review Boards, to decide whether research protocols are "ethical" according to vague federal regulations. Without IRB approval, the protocol cannot be investigated. The empirical evidence regarding this system demonstrates that IRBs render deeply inconsistent and inaccurate outcomes. This Article argues that the lack of due process in the IRB system is to blame for such arbitrary agency action. By juxtaposing the levels of process required for IRB approval or research with FDA new drug approval--agency action involving similar interests--this Article highlights that IRBs …

Enlightened Regulatory Capture, David Thaw

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Regulatory capture generally evokes negative images of private interests exerting excessive influence on government action to advance their own agendas at the expense of the public interest. There are some cases, however, where this conventional wisdom is exactly backwards. This Article explores the first verifiable case, taken from healthcare cybersecurity, where regulatory capture enabled regulators to harness private expertise to advance exclusively public goals. Comparing this example to other attempts at harnessing industry expertise reveals a set of characteristics under which regulatory capture can be used in the public interest. These include: 1) legislatively-mandated adoption of recommendations by an advisory …

Does The Constitution Protect Abortions Based On Fetal Anomaly?: Examining The Potential For Disability-Selective Abortion Bans In The Age Of Prenatal Whole Genome Sequencing, Greer Donley

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This Note examines whether the state or federal government has the power to enact a law that prevents women from obtaining abortions based on their fetus’s genetic abnormality. Such a ban has already been enacted in North Dakota and introduced in Indiana and Missouri. I argue below that this law presents a novel state intrusion on a woman’s right to obtain a pre-viability abortion. Moreover, these pieces of legislation contain an outdated understanding of prenatal genetic testing—the landscape of which is quickly evolving as a result of a new technology: prenatal whole genome sequencing. This Note argues that the incorporation …

Behavioral Genetics And The Best Interests Of The Child Decision Rule, David J. Herring

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This article proposes that modern child custody law should be reassessed in light of recent scientific findings. Judicial determinations of custody use the "best interests of the child" rule. The rule is justified to a large extent by the goal of maximizing child developmental outcomes. The assumption is that a child whose "best interests" are protected stands a better chance of becoming a socially well-adjusted, productive and prosperous citizen.

Recent child development studies have shown that so-called "shared environment," or home environment factors have little effect on child development so long as the shared environment is minimally adequate. Genetics and …

Choice, Conscience, And Context, Mary Crossley

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Building on Professor Michael H. Shapiro's critique of arguments that some uses of new reproductive technologies devalue and use persons inappropriately (which is part of a Symposium on New Reproductive Technologies), this work considers two specific practices that increasingly are becoming part of the new reproductive landscape: selective reduction of multiple pregnancy and prenatal genetic testing to enable selective abortion. Professor Shapiro does not directly address either practice, but each may raise troubling questions that sound suspiciously like the arguments that Professor Shapiro sought to discredit. The concerns that selective reduction and prenatal genetic screening raise, however, relate not to …

Opinion Letter As To The Patentability Of Certain Inventions Associated With The Identification Of Partial Cdna Sequences, Rebecca S. Eisenberg, Robert P. Merges

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You have asked for our legal opinion on the patentability of inventions claimed in U.S. patent applications 07/716,831, filed June 21, 1991 (the '831 application, or .'831"), 07/837,195, filed September 25, 1992 ("'195"), and 07/952,911, filed February 12, 1993 (."911"), all filed in the name of Craig Venter and others and assigned to the National Institutes of Health "(NIH)." We understand that NIH has abandoned these patent applications and has no present intention of filing similar applications in the future, but that NIH remains interested in the patenting of human DNA sequences from a broader public policy perspective. We have …

Reply To Comments On The Patentability Of Certain Inventions Associated With The Identification Of Partial Cdna Sequences, Rebecca S. Eisenberg, Robert P. Merges

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A brief reply is in order to clarify our position on the patenting of research tools. We stand by the statement that "there are reasons to be wary of patents on research tools," but that statement should not be understood as a broad condemnation of patents on research tools in all contexts. Indeed, immediately after the cited language our opinion letter acknowledges that withholding patent protection from research tools could undermine private incentives to develop research tools and to make them available to investigators or lead to greater reliance on trade secrecy. Unlike the government, which purports to pursue patent …