Charlotte Tschider (Loyola U Chicago Law) and Cynthia M. Ho (Loyola U Chicago Law) have posted “Artificial Intelligence and Intellectual Property in Healthcare Technologies” (Ch. 11: Artificial intelligence and intellectual property in healthcare technologies, in Research Handbook on Health, AI and the Law (Edgar, ed. Barry Solaiman & I. Glenn Cohen), https://doi.org/10.4337/9781802205657.00018) on SSRN. Here is the abstract:

Artificial intelligence (AI) healthcare technologies involve a wide variety of AI innovations that could potentially qualify for intellectual property (IP) protection, corresponding to multiple forms of protection. In addition, protection for AI raises novel issues that may require modifying existing laws. This chapter examines how current IP law applies to human-generated AI creations and policy issues that should be considered as organisations and countries re-examine IP policy. After this brief introduction, section 2 provides an introduction to IP, section 3 details AI in healthcare to better understand IP issues and section 4 addresses issues AI owners will likely encounter in IP strategy. Finally, section 5 addresses policy issues for lawmakers to consider.

Doni Bloomfield (Fordham U Law) has posted “AI and Biosecurity: The Need for Governance” on SSRN. Here is the abstract:

Great benefits to humanity will likely ensue from advances in artificial intelligence models trained on or capable of meaningfully manipulating substantial quantities of biological data, from speeding up drug and vaccine design to improving crop yields. But as with any powerful new technology, such biological models will also pose considerable risks. Because of their general-purpose nature, the same biological model able to design a benign viral vector to deliver gene therapy could be used to design a more pathogenic virus capable of evading vaccine-induced immunity. Voluntary commitments among developers to evaluate biological models’ potential dangerous capabilities are meaningful and important but cannot stand alone. We propose that national governments, including the United States, pass legislation and set mandatory rules that will prevent advanced biological models from substantially contributing to large-scale dangers, such as the creation of novel or enhanced pathogens capable of causing major epidemics or even pandemics.

Alessandro Blasimme (ETH Zurich) has posted “Machine Learning in Paediatrics and the Childs’s Right to An Open Future” on SSRN. Here is the abstract:

Machine Learning (ML)-driven diagnostic systems for mental and behavioural paediatric conditions can have profound implications for child development, children’s image of themselves and their prospects for social integration.

The use of machine learning (ML) in biomedical research, clinical practice and public health is set to radically transform medicine. Ethical challenges associated to such transformation are particularly salient in the case of vulnerable or dependent patients. One relatively neglected ethical issue in this space is the extent to which the clinical implementation of ML-based predictive analytics is bound to erode what philosopher Joel Feinberg has defined as children’s right to an open future.

An ethical analysis of how the unprecedented predictive power of ML diagnostic systems can affect a child’s right to an open future has not yet been undertaken. In this paper, I illustrate the right to an open future and explain its relevance in relation to diagnostic uses of ML in paediatric medicine, with a particular focus on Attention-Deficit/Hyperactivity Disorder and autism.

ML-based diagnostic tools focused on brain imaging run the risk of objectifying mental and behavioural conditions as brain abnormalities, even though the neuropathological mechanisms causing such abnormalities at the level of the brain are far from clear.

Gains in automating psychiatric diagnosis have to be weighed against the risks that ML-driven diagnoses may affect a child’s capacity to uphold a sense of self-worth and social integration.

Charlotte Tschider (Loyola University Chicago School of Law) has posted “Prescribing Exploitation” (Maryland Law Review, Forthcoming 2023) on SSRN. Here is the abstract:

Patients are increasingly reliant temporarily, if not indefinitely, on connected medical devices and wearables, many of which use artificial intelligence (AI) infrastructures and physical housing that directly interacts with the human body. The automated systems that drive the infrastructures of medical devices and wearables, especially those using complex AI, often use dynamically inscrutable algorithms that may render discriminatory effects that alter paths of treatment and other aspects of patient welfare.

Previous contributions to the literature, however, have not explored how AI technologies animate exploitation of medical technology users. Although all commercial relationships may exploit users to some degree, some forms of health data exploitation exceed the bounds of normative acceptability. The factors that illustrate excessive exploitation that should require some legal intervention include: 1) existence of a fiduciary relationship or approximation of such a relationship, 2) a technology-user relationship that does not involve the expertise of the fiduciary, 3) existence of a critical health event or health status requiring use of a medical device, 4) ubiquitous sensitive data collection essential to AI functionality, 5) lack of reasonably similar analog technology alternatives, and 6) compulsory reliance on a medical device.

This paper makes three key contributions to existing literature. First, this paper establishes the existence of a type of exploitation that is not only exacerbated by technology but creates additional risk by its use. Second, this paper illustrates the need for cross-disciplinary engagement across privacy scholarship and AI ethical goals that typically involve representative data collection for fairness and safety. This paper then illustrates how a modern information fiduciary model can neutralize patient exploitation risk when exploitation exceeds normative bounds of community acceptability.

Recommended.

W. Nicholson Price II (University of Michigan Law School) has posted “Distributed Governance of Medical AI” (25 SMU Sci. & Tech. L. Rev. (Forthcoming 2022)) on SSRN. Here is the abstract:

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 for patient care. To make the situation even more complicated, AI is unlikely to go through the centralized review and validation process that other medical technologies undergo, like drugs and most medical devices. Even if it did go through those centralized processes, ensuring high-quality performance across a wide variety of settings, including poorly resourced settings, is especially challenging for such centralized mechanisms. What are policymakers to do? This short Essay argues that the diffusion of medical AI, with its many potential benefits, will require policy support for a process of distributed governance, where quality evaluation and oversight take place in the settings of application—but with policy assistance in developing capacities and making that oversight more straightforward to undertake. Getting governance right will not be easy (it never is), but ignoring the issue is likely to leave benefits on the table and patients at risk.

Mirko Forti (Scuola Superiore Sant’Anna di Pisa – School of Law) has posted “The Deployment of Artificial Intelligence Tools in the Health Sector: Privacy Concerns and Regulatory Answers within the GDPR” on SSRN. Here is the abstract:

This article examines the privacy and data protection implications of the deployment of machine learning algorithms in the medical sector. Researchers and physicians are developing advanced algorithms to forecast possible developments of illnesses or disease statuses, basing their analysis on the processing of a wide range of data sets. Predictive medicine aims to maximize the effectiveness of disease treatment by taking into account individual variability in genes, environment, and lifestyle. These kinds of predictions could eventually anticipate a patient’s possible health conditions years, and potentially decades, into the future and become a vital instrument in the future development of diagnostic medicine. However, the current European data protection legal framework may be incompatible with inherent features of artificial intelligence algorithms and their constant need for data and information. This article proposes possible new approaches and normative solutions to this dilemma.

Walter G. Johnson (RegNet, Australian National University) has posted “Flexible Regulation for Dynamic Products? The Case of Applying Principles-Based Regulation to Medical Products Using Artificial Intelligence” (Law, Innovation and Technology 14(2)) on SSRN. Here is the abstract:

Emerging technologies including artificial intelligence (AI) enable novel products to have dynamic and even self-modifying designs, challenging approval-based products regulation. This article uses a proposed framework by the US Food and Drug Administration (FDA) to explore how flexible regulatory tools, specifically principles-based regulation, could be used to manage ‘dynamic’ products. It examines the appropriateness of principles-based approaches for managing the complexity and fragmentation found in the setting of dynamic products in terms of regulatory capacity and accountability, balancing flexibility and predictability, and the role of third parties. The article concludes that successfully deploying principles-based regulation for dynamic products will require taking serious lessons from the global financial crisis on managing complexity and fragmentation while placing equity at the centre of the framework.

Frank Griffin (University of Arkansas) has posted “Artificial Intelligence and Liability in Health Care” (31 Health Matrix: Journal of Law-Medicine 65-106 (2021)) on SSRN. Here is the abstract:

Artificial intelligence (AI) is revolutionizing medical care. Patients with problems ranging from Alzheimer’s disease to heart attacks to sepsis to diabetic eye problems are potentially benefiting from the inclusion of AI in their medical care. AI is likely to play an ever- expanding role in health care liability in the future. AI-enabled electronic health records are already playing an increasing role in medical malpractice cases. AI-enabled surgical robot lawsuits are also on the rise. Understanding the liability implications of AI in the health care system will help facilitate its incorporation and maximize the potential patient benefits. This paper discusses the unique legal implications of medical AI in existing products liability, medical malpractice, and other law.

Sara Gerke (Harvard University – Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics), Boris Babic, Theodoros Evgeniou (INSEAD), and I. Glenn Cohen (Harvard Law School) have posted “The Need for a System View to Regulate Artificial Intelligence/Machine Learning-Based Software as Medical Device” (NPJ Digit Med. 2020 Apr 7;3:53) on SSRN. Here is the abstract:

Artificial intelligence (AI) and Machine learning (ML) systems in medicine are poised to significantly improve health care, for example, by offering earlier diagnoses of diseases or recommending optimally individualized treatment plans. However, the emergence of AI/ML in medicine also creates challenges, which regulators must pay attention to. Which medical AI/ML-based products should be reviewed by regulators? What evidence should be required to permit marketing for AI/ML-based software as a medical device (SaMD)? How can we ensure the safety and effectiveness of AI/ML-based SaMD that may change over time as they are applied to new data? The U.S. Food and Drug Administration (FDA), for example, has recently proposed a discussion paper to address some of these issues. But it misses an important point: we argue that regulators like the FDA need to widen their scope from evaluating medical AI/ML-based products to assessing systems. This shift in perspective—from a product view to a system view—is central to maximizing the safety and efficacy of AI/ML in health care, but it also poses significant challenges for agencies like the FDA who are used to regulating products, not systems. We offer several suggestions for regulators to make this challenging but important transition.

Derek E. Bambauer (University of Arizona – James E. Rogers College of Law) has posted “Cybersecurity for Idiots” (106 Minnesota Law Review Headnotes __ (2021 Forthcoming)) on SSRN. Here is the abstract:

Cybersecurity remains a critical issue facing regulators, particularly with the advent of the Internet of Things. General-purpose security regulators such as the Federal Trade Commission continually struggle with limited resources and information in their oversight. This Essay contends that a new approach to cybersecurity modeled on the negligence per se doctrine in tort law will significantly improve cybersecurity and reduce regulatory burdens. It introduces a taxonomy of regulators based upon the scope of their oversight and the pace of technological change in industries within their purview. Then, the Essay describes negligence per se for cybersecurity, which establishes a floor for security precautions that draws upon extant security standards. By focusing on the worst offenders, this framework improves notice to regulated entities, reduces information asymmetries, and traverses objections from legal scholars about the cost and efficacy of cybersecurity mandates. The Essay concludes by offering an emerging case study for its approach: regulation of quasi-medical devices by the Food and Drug Administration. As consumer devices increasingly offer functionality for both medical and non-medical purposes, the FDA will partly transition to a general-purpose regulator of information technology, and the negligence per se model can help the agency balance security precautions with promoting innovation.