NEW INNOVATION MODELS IN MEDICAL AI.

• Author: Price, W. Nicholson, II
• Position: Medical artificial intelligence legal issues

ABSTRACT

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 shaping medical innovation: patent law, FDA regulation, and health insurance reimbursement. Second, and perhaps related, a different set of innovation stakeholders, including health systems and insurers, are conducting their own research and development in these areas for their own use without waiting for commercial product developers to innovate for them. The activities of these innovators have implications for health innovation policy and scholarship. Perhaps most notably, data possession and control play a larger role in determining capacity to innovate in this space, while the ability to satisfy the quality standards of regulators and payers plays a smaller role relative to more familiar biomedical innovations such as new drugs and devices.

TABLE OF CONTENTS INTRODUCTION I. NEW TECHNOLOGIES AND NEW INNOVATORS A. Health Systems B. Insurers C. Venture Capital Investments II. DIMINISHED LEGAL REGIMES: QUALITY OVERSIGHT AND INCENTIVES A. FDA Regulation 1. The Limits of FDA's Regulatory Authority 2. Enforcement Discretion and its Limits 3. Implications for Medical AI B. Patent Law 1. Patent Eligibility 2. Patent Disclosure Requirements C. Insurance Reimbursement III. Implications A. Data Control B. Customized Products C. Decreased Quality Oversight CONCLUSION INTRODUCTION

Innovation in medical AI is exploding. Every week sees new research papers presenting new algorithms, new companies launching new products, and new possibilities for change. AI products promise to recognize and diagnose skin cancer, to identify eye disease, to find kidney stones, to locate brain hemorrhages, and to quickly detect COVID-19, among many other possibilities. (2) These technologies are likely to change the practice of medicine by increasing the capabilities of care providers in many areas. Products like these also fit--if somewhat uncomfortably (3)--into a capacious understanding of what new medical technology looks like and how we expect it to be regulated. But these are not the only AI products with the potential to transform medicine.

Other AI innovations look quite unlike typical medical devices, yet they also have the potential to transform health care in different ways. (4) A seemingly mundane example is AI-powered scheduling software, which predicts the ebb and flow of patients within the health care system and allocates staff to most effectively meet those patients' needs. Such products do not directly diagnose or treat patients, but they could increase the capacity of a stretched system and thereby save lives. Other products improve quality of care by predicting the likelihood that a patient will be readmitted to the hospital within a month (so that health care providers can work with patients to prevent that undesirable outcome) or by identifying the risk of a patient developing sepsis (so that rapid-response teams can intervene early). These functions are extremely valuable to the health care system, and all are amenable to AI assistance.

For these forms of AI innovation, however, the traditional policy levers that shape much biomedical innovation--patents, FDA regulation, and insurance reimbursement (5)--play more uncertain and attenuated roles. Although many innovators are actively pursuing patents, the patentability of medical AI under U.S. law is unclear, making it risky to enforce AI patents that a court might hold invalid. Patents may also be less important to would-be innovators because AI innovations are often easy to protect via trade secrecy. Some of these technologies may get less scrutiny from FDA, perhaps because they do not fit within the statutory definition of medical devices, or perhaps because they fall within categories for which FDA has traditionally exercised discretion not to enforce its authorities. And insurance reimbursement, which normally helps both to drive the development of medical technology and to provide some quality-related oversight, plays little role here, as these products are typically not directly reimbursable. The usual incentives of insurance reimbursements or patent law exclusivity are thus lower for these forms of innovation, but barriers to entry from FDA or insurer oversight may be lower as well. To be sure, some medical AI innovators do seek patents and FDA approval or clearance. Nonetheless, medical AI innovation faces a substantially different legal landscape than more conventional biomedical innovation, such as the development of physical devices or drugs. (6)

Within this landscape, innovation by end-users of medical AI is flourishing. Health systems (including individual academic medical centers and hospitals) and insurers are not only developing and using AI technologies themselves but also setting up in-house venture capital funds to invest in AI startups. Health systems and insurers have different incentives than conventional biomedical innovators (such as drug and device manufacturers). Their primary purpose for innovating is not to sell innovative products to customers. Instead, they are developing innovative AI tools to enhance their main business of providing, insuring, or facilitating health care. In the theoretical model pioneered by Eric von Hippel, (7) they are "user innovators" rather than seller innovators. (8) They benefit directly from using their innovations without having to sell or license them to others (though they may do both). User innovators are more likely to focus on their own specific needs and circumstances, creating more customized products for their own use, while seller innovators are more likely to produce standardized products designed for sale to a broader market of users. (9)

Of course, users are not the only innovators of medical AI. Large technology companies are developing AI-powered health software for sale to users, as are small startups. And the IT infrastructure providers of health care, the makers of electronic health record (EHR) software, are themselves developing AI algorithms and incorporating them into EHR products. But the innovation incentives for commercial product developers are somewhat more familiar and not our focus here.

The rise of user innovation in biomedical AI has several implications for policymakers. First, it is worth considering that the different legal landscape in this setting may be making room for different kinds of innovators to develop different forms of innovation. Just as the ordinarily robust legal regimes that provide patents, FDA regulation, and insurance reimbursement for new technologies shape biomedical innovation in drugs and physical devices, (10) the smaller roles these regimes play for medical AI may shape the different forms of innovation that we observe in this space, perhaps making more room for user innovation. Second, the availability and control of data confer a significant comparative advantage on some innovators in this field, including large institutional user innovators. AI is easier to develop in-house for health systems or insurers with their own large stocks of patient health information. Smaller institutions, or commercial firms without access to such data, may find it harder to compete. Third, a proliferation of biomedical user innovators brings challenges as well as opportunities. AI innovations tailored to one institution's needs and circumstances may not be suitable for other potential users facing different needs and circumstances.11 Even larger institutional datasets are limited in scope, limiting the power and generalizability of AI solutions based on those datasets. Problems of error, overfitting, or data biases might go unrecognized without effective oversight from FDA or insurers. These effects have broader impacts on the quality, cost, and equity of medical AI more generally.

The rest of this Article proceeds in three Parts. Part II canvasses the landscape of user innovation in medical AI and describes the novel innovators involved, focusing on the roles and incentives of health systems and health insurers. Part III looks to the primary regimes that scholars have generally recognized as shaping biomedical innovation--patent law, FDA oversight, and insurance reimbursement--and explains how their role is diminished or uncertain for these technologies. Part IV addresses the implications of these analyses, including concerns around the availability of data, the customization of local solutions to local problems, and risks of difficult-to-detect quality concerns. A few brief thoughts conclude.

1. NEW TECHNOLOGIES AND NEW INNOVATORS

   AI powers a proliferating set of new medical technologies. Some AI tools are directly involved in patient care, such as systems that diagnose medical issues or monitor patients for signs of medical problems that can be aided by early intervention. Some function more in the background, such as algorithms to predict the likelihood of future adverse outcomes. Still, others are even further removed from the point of patient care, monitoring and shaping the flow of patients or providers across a hospital to improve system efficiency or to increase the volume of care provided. Each of these avenues has the potential to impact the health care landscape and the experience of patient care.

   The technologies we consider...