1,721,053 research outputs found
Am I My Son? Human Clones and the Modern Family
No full textAs increasingly complex assisted reproductive technologies (ART) become available, legal and social conceptions of family become ambiguous and sometimes misaligned. The as-yet unrealized technology of cloning provides the clearest example of this confusion: is the legal parent of a clone the individual cloned, or are that individual‘s parents also the parents of the clone? These issues have been generally obscured by the debates around the deployment of ART, especially cloning; far less consideration has been given to the way these new technologies impact the way we think about and develop law on the relationships between genetic, social, gestational, and legal parenthood. This article considers these issues in depth, looking at competing common-law frameworks for determining parentage, the statutory framework of parentage, and deeper theoretical concerns underlying the area. The article concludes that an intent-based framework, with at least some external limitations, most accurately matches law to social views of parents using new forms of ART
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Illegal Sex Toy Patents
No full textIn Patenting Pleasure, Professors Sarah Rajec and Andrew Gilden highlight a surprising incongruity: while many areas of U.S. law are profoundly hostile to sexuality in general and the technology of sex in particular, the patent system is not. Instead, the U.S. Patent and Trademark Office (USPTO) has over the decades issued thousands of patents on sex toys—from vibrators to AI, and everything in between. This incongruity is especially odd because patent law has long incorporated a doctrine that specifically tied patentability to the usefulness of the invention, and up until the end of the 20th century one strand of that doctrine held that inventions “injurious to society” failed the utility test. And until about that time—and in some states and localities, even today—the law was exceptionally clear that sex toys were immoral and illegal. Patents issued nonetheless. How did inventors show that their sex toys were useful, despite being barred from relying on their most obvious use? Gilden and Rajec examine hundreds of issued patents to weave an engrossing narrative about sex, patents, and the law
Expired Patents, Trade Secrets, and Stymied Competition
No full textPatents and trade secrecy have long been considered substitute incentives for innovation. When inventors create a new invention, they traditionally must choose between the two. And if inventors choose to patent their invention, society provides strong legal protection in exchange for disclosure, with the understanding that the protection has a limit: it expires twenty years from the date of filing. At that time, the invention is opened to the public and exposed to competition. This story is incomplete. Patent disclosure is weak and focuses on one technical piece of an invention—but that piece is often only a part of the market-relevant innovation. Patent-holding innovators use various tactics to distort the patent bargain and prolong effective monopolies beyond the patent’s expiration date. These tactics include using patented inventions to generate secret information, relying on the timing difference between patent filing and product marketing to make disclosure nearly irrelevant, and tying secret components to patented frameworks. While these phenomena have been noted before, this Article joins them together as examples of ways that innovators avoid the competition-promoting function of patent expiration, ultimately limiting the benefit the public receives from patented inventions. It also suggests that the most problematic cases likely involve markets where additional factors, such as regulation or other market irregularities, require that goods be interchangeable. Finally, it proposes the concept of economic enablement: patentees may have a responsibility to enable not just the bare technical invention disclosed in a patent, but rather the minimum information necessary to exploit commercially the patented invention. Against the background of the newly enacted Federal Defend Trade Secrets Act, courts and scholars alike should examine the boundaries between trade secrets and patents to ensure that the overlap does not distort the policy goal of incentivizing and promoting both innovation and competition
Medical AI and Contextual Bias
No full textArtificial 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 to train medical AI in high-resource contexts, such as academic medical centers. And put simply, care is different in high-resource settings than it is in low-resource settings such as community health centers or rural providers in less-developed countries. Patient populations differ, as do the resources available to administer treatment and the resources available to pay for that treatment. This development pattern will lead to decreases in the quality of the algorithm’s recommendations, reflected in problematic care and increased costs. Perniciously, such quality problems in low-resource contexts are likely to go unrecognized for exactly the same reasons that promote algorithmic training in high-resource contexts. Solutions are not trivial. Labeling products the same way that drugs are labeled is unlikely to work, and truly addressing the problem may require a combination of public investment in data to train medical AI and regulatory requirements for cross-context validation. Nevertheless, if black-box medicine is to achieve its goal of bringing excellent medicine to broad sets of patients, the problem of contextual bias should be recognized and addressed sooner rather than later
Making Do in Making Drugs: Innovation Policy and Pharmaceutical Manufacturing
No full textDespite increasing recalls, contamination events, and shortages, drug companies continue to rely on outdated manufacturing plants and processes. Drug manufacturing\u27s inefficiency and lack of innovation stand in stark contrast to drug discovery, which is the focus of a calibrated innovation policy that combines patents and FDA regulation. Pharmaceutical manufacturing lags far behind the innovative techniques found in other industries due to high regulatory barriers and ineffective intellectual property incentives. Among other challenges, although manufacturers tend to rely on trade secrecy because of the difficulty in enforcing patents on manufacturing processes, trade secrecy provides limited incentives for innovation. To increase those incentives, this Article suggests several direct regulatory reforms and proposes novel ways to use those reforms to improve innovation policy in drug manufacturing and beyond. For example, the FDA could operate a system of temporary market exclusivity for manufacturing innovation parallel to the patent system.Alternatively, the FDA could require disclosure of manufacturing methods to drive the industry from opacity and trade secrecy towards transparency and patent protection for innovation. Overall, the potentially immense economic and health benefits from more innovative manufacturing in the drug industry suggest that manufacturing may be a profitable target of innovation policy in other highly regulated industries and that manufacturing in general deserves a more prominent place in innovation policy and theory
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Problematic Interactions between AI and Health Privacy
No full textThe 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 non-health data to infer health information. The result is an unfortunate anti- synergy: privacy protections are weak and illusory, but rules meant to protect privacy hinder other socially valuable goals. This state of affairs creates biases in health AI, privileges commercial research over academic research, and is ill-suited to either improve health care or protect patients’ privacy. The ongoing dysfunction calls for a new bargain between patients and the health system about the uses of patient data
Clinicians in the Loop of Medical AI
Full text linkAs 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
Black-Box Medicine
No full textPersonalized medicine, where Big Data meets Big Health, has been hailed as the next leap forward in health care, but that leap raises tremendous challenges for our current policy landscape. This Article is the first to label the phenomenon of black-box medicine, a version of personalized medicine in which researchers use sophisticated algorithms to examine huge troves of health data, finding complex, implicit relationships and making individualized assessments for patients. This new form of medicine offers potentially immense benefits but faces major hurdles both in development and in application. Development requires high investment; firms must develop new datasets, models, and validations, which are all nonrivalrous information goods that require incentives for welfare-optimizing levels of development. However, current innovation policy lacks the necessary incentives and instead pushes firms in socially suboptimal directions. Black-box medicine also raises significant challenges with respect to privacy, regulation, and commercialization. This Article describes black-box medicine, explains its differences-in-kind from current forms of medicine, and briefly explores the landscape of policy challenges ahead
Big Data, Patents, and the Future of Medicine
No full textBig data has tremendous potential to improve health care. Unfortunately, intellectual property law isn’t ready to support that leap. In the next wave of data- driven medicine, black-box medicine, researchers use sophisticated algorithms to examine huge troves of health data, finding complex, implicit relationships and making individualized assessments for patients. Black-box medicine offers potentially immense benefits, but also requires substantial high investment. Firms must develop new datasets, models, and validations, which are all nonrivalrous information goods with significant spillovers, requiring incentives for welfare-optimizing investment. Current intellectual property law fails to provide adequate incentives for black- box medicine. The Supreme Court has sharply restricted patentable subject matter in the recent Prometheus, Myriad, and Alice cases, and what might still be patentable is limited by the statutory requirements of written description and enablement. Other incentives for investment, such as trade secrecy or prizes, fail to fill the gaps. These limits push firms away from using big data in medicine to solve big problems, and push firms toward small-scale incremental innovation. Small tweaks to doctrine will help, but are not enough. Instead, the big data needed to support transformative medical innovation should be considered as infrastructure for innovation and should be the focus of substantial public effort
Can Informed Consent Solve AI Bias?
Full text linkArtificial intelligence (AI) is moving increasingly rapidly into health care (as indeed into everything else). But it has problems there (as indeed everywhere else!). What’s to be done, in particular, about the deeply embedded biases along racial and other lines that permeate the whole world of health and, as such, are likely to be encoded in AI?
Khiara Bridges gives an answer that seems mild but carries roots of revolution. In Race in the Machine: Racial Disparities in Health and Medical AI, she argues that informed consent is a key lever to pull in fighting these racial disparities. But not because informed consent—at present, mostly a formality, a begrudging nod to autonomy—will fix the problem in its current state. Instead, Bridges argues, informed consent, beefed up and focused on conveying the brutal truth about encoded racial disparities, can form the foundation for revolutionary social changes in health care, health, and beyond. Curious? Read on
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