353 research outputs found
Artificial Intelligence in the Medical System: Four Roles for Potential Transformation
No full textArtificial 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 is necessary to identify and address major hurdles to the responsible development and deployment of medical AI
Artificial Intelligence in the Medical System: Four Roles for Potential Transformation
No full textArtificial 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 own challenges, and an understanding of the four roles is necessary to identify and address major hurdles to the responsible development and deployment of medical AI
The Cost of Novelty
No full textPatent law tries to spur the development of new and better innovative technology. But it focuses much more on “new” than “better”—and it turns out that “new” carries real social costs. I argue that patent law promotes innovation that diverges from existing technology, either a little (what I call “differentiating innovation”) or a lot (“exploring innovation”), at the expense of innovation that tells us more about existing technology (“deepening innovation”). Patent law’s focus on newness is unsurprising, and fits within a well-told narrative of innovative diversity accompanied by market selection of the best technologies. Unfortunately, innovative diversity brings not only the potential benefits of technological advances but also the costs: incompatibility between different technologies; a spread-out, shallow pool of knowledge; and the underlying costs of developing parallel technologies that aren’t actually better. These costs matter. Biomedical innovation illustrates the high costs of divergence. Although pharmaceuticals are touted as a poster child for patents, the world is rife with me-too drugs that drive up costs with little to show for it. Biomedical innovation often suffers from a particular trap: Patent incentives push innovators toward “new,” but incentives from Food and Drug Administration approval and insurer reimbursement push innovators toward “not too new.” In this space, artificially constricted markets do a poor job of selecting better technologies. The result is a proliferation of technologies that are “new for the sake of new,” giving us the costs of divergence without much in the way of benefits. This Essay presents an original spectrum of innovative divergence, illuminates how various patent doctrines drive divergence, and lays out the substantial costs of divergence through biomedical examples. It analyzes the complex interactions between three different incentives for biomedical innovation and presents policy prescriptions to help avoid the trap of “new for the sake of new.” In the process, it lays out how innovation scholars and policymakers alike should take into account the cost of novelty
The Cost of Novelty
No full textPatent law tries to spur the development of new and better innovative technology. But it focuses much more on “new” than “better”—and it turns out that “new” carries real social costs. I argue that patent law promotes innovation that diverges from existing technology, either a little (what I call “differentiating innovation”) or a lot (“exploring innovation”), at the expense of innovation that tells us more about existing technology (“deepening innovation”). Patent law’s focus on newness is unsurprising, and fits within a well-told narrative of innovative diversity accompanied by market selection of the best technologies. Unfortunately, innovative diversity brings not only the potential benefits of technological advances but also the costs: incompatibility between different technologies; a spread-out, shallow pool of knowledge; and the underlying costs of developing parallel technologies that aren’t actually better. These costs matter. Biomedical innovation illustrates the high costs of divergence. Although pharmaceuticals are touted as a poster child for patents, the world is rife with me-too drugs that drive up costs with little to show for it. Biomedical innovation often suffers from a particular trap: Patent incentives push innovators toward “new,” but incentives from Food and Drug Administration approval and insurer reimbursement push innovators toward “not too new.” In this space, artificially constricted markets do a poor job of selecting better technologies. The result is a proliferation of technologies that are “new for the sake of new,” giving us the costs of divergence without much in the way of benefits. This Essay presents an original spectrum of innovative divergence, illuminates how various patent doctrines drive divergence, and lays out the substantial costs of divergence through biomedical examples. It analyzes the complex interactions between three different incentives for biomedical innovation and presents policy prescriptions to help avoid the trap of “new for the sake of new.” In the process, it lays out how innovation scholars and policymakers alike should take into account the cost of novelty
Are Trade Secrets Delaying Biosimilars?
No full textOn 6 March 2015, the United States Food and Drug Administration (FDA) approved, under the Biologics Price Competition and Innovation Act (BPCIA), a biosimilar of filgrastim (Neupogen), for treating chemotherapy-caused neutropenia (1). Although this action represents a step toward cheaper medical treatments, it masks systemic problems. Not only has it taken 5 years since the BPCIA\u27s passage (2), but economists estimate that even by 2020, biosimilar competition will reduce consumer prices only modestly (3). Why will price competition be so lacking? One key reason is the barrier to competitive entry created by trade secrecy in biologics manufacturing
Distributed Governance of Medical AI
No full textArtificial 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
Will mRNA Technology Companies Spawn Innovation Ecosystems?
No full textThe mRNA technologies that helped rapidly create effective Covid-19 vaccines could become technology platform businesses, which has tremendous implications for players the world of drug development. These platforms could attract other companies interested in exploiting their advantages to develop other drugs. But all the stakeholders — platform owners, external pharmaceutical and biotech companies, policymakers, and regulators — will have to make a variety of choices
Does Whole-Genome Sequencing Circumvent Gene Patents?
No full text[Excerpt] Last month, the Supreme Court voted to hear the case of Association for Molecular Pathology v. Myriad Genetics to consider the question whether human genes are patentable. The plaintiffs—doctors, patients, researchers, and the American Civil Liberties Union—have challenged Myriad’s patents on the breast cancer genes BRCA1 and BRCA2, which cover, among other things, isolated DNA molecules with the sequences of those genes. A federal district court in New York ruled that the patent claims on isolated DNA molecules were invalid, but that ruling was reversed on appeal by the Federal Circuit in D.C. The Supreme Court decided to review the Federal Circuit’s decision and will likely rule on whether isolated human gene sequences are patentable next summer. This case has profound implications for biotechnology, and diagnostics, as well as the emerging field of personalized medicine. Among the fascinating issues that will likely be addressed is whether WGS—an essential foundation for truly personalized medicine—violates human gene patents
Clinicians in the Loop of Medical AI
No full textAs medical AI begins to mature as a health-care tool, the task of governance grows increasingly important. Ensuring that medical AI works, works where it’s used, and works for the patient in the moment is a challenging, multifaceted task. Some of this governance can be centralized—in review by FDA or by national accreditation labs, for instance. Some must be local, performed by the hospital or health system about to use the product in their own, unique environment. But a large amount of governance is left to the individual provider in the room, the human in the loop who presumably knows the patient and the health system environment, and who can ensure that the AI system is being used in a safe and effective manner. This is a hefty burden, and a growing body of empirical research shows that physicians and other providers are poorly prepared to carry this burden. How should policymakers and industry leaders develop standards for performance that account for the variability of humans in the loop and the variation among situations they will face? The notion that the final responsibility belongs to the physician poorly reflects the reality of modern medical technology and practice. Policymakers will need to come to grips with this new reality if they aim to ensure the safe, effective use of AI accessible to patients across the entire spectrum of the health-care system
Clinicians in the Loop of Medical AI
No full textAs medical AI begins to mature as a health-care tool, the task of governance grows increasingly important. Ensuring that medical AI works, works where it’s used, and works for the patient in the moment is a challenging, multifaceted task. Some of this governance can be centralized—in review by FDA or by national accreditation labs, for instance. Some must be local, performed by the hospital or health system about to use the product in their own, unique environment. But a large amount of governance is left to the individual provider in the room, the human in the loop who presumably knows the patient and the health system environment, and who can ensure that the AI system is being used in a safe and effective manner. This is a hefty burden, and a growing body of empirical research shows that physicians and other providers are poorly prepared to carry this burden. How should policymakers and industry leaders develop standards for performance that account for the variability of humans in the loop and the variation among situations they will face? The notion that the final responsibility belongs to the physician poorly reflects the reality of modern medical technology and practice. Policymakers will need to come to grips with this new reality if they aim to ensure the safe, effective use of AI accessible to patients across the entire spectrum of the health-care system
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