Balkinization: Four Roles for Artificial Intelligence in the Medical System

Four Roles for Artificial Intelligence in the Medical System

Guest Blogger

W. Nicholson Price II

For the Symposium on The Law And Policy Of AI, Robotics, and Telemedicine In Health Care.

How will artificial intelligence (AI) change medicine? AI, powered by “big data” in health, promises to transform medical practice, but specifics remain inchoate. Reports that AI performs certain tasks at the level of specialists stoke worries that AI will “replace” physicians. These worries are probably overblown; AI is unlikely to replace many physicians in the foreseeable future. A more productive set of questions considers how AI and physicians should interact, including how AI can improve the care physicians deliver, how AI can best enable physicians to focus on the patient relationship, and how physicians should review the recommendations and predictions of AI. Answering those questions requires clarity about the larger function of AI: not just what tasks AI can do or how it can do them, but what role it will play in the context of physicians, other patients, and providers within the overall medical system. Medical AI can improve for patients and improve the practice of medicine for providers—as long as its development is supported by an understanding of what role it can and should play. Four different roles each have the possibility to be transformative for providers and patients: AI can push the frontiers of medicine; it can replicate and democratize medical expertise; it can automate medical drudgery; and it can allocate medical resources.

Pushing frontiers

The headline promise of artificial intelligence is that it will push the frontiers of medical care, enabling a level of precision and personalization previously unavailable. Some medical AI aims to use underlying patterns of patient characteristics—biological, social, or environmental—to tailor treatment. In this role, AI increases provider capabilities and can result in better patient care. For instance, AI can power “artificial pancreas” devices that combine a continuous glucose monitor, a subcutaneous insulin pump, and a controller. The AI enables the controller to “learn” the glucose response of a particular patient and to tailor insulin dosage to keep glucose levels within safe limits over the course of the day. Ideally, AI-controlled artificial pancreases can perform better than earlier technology because the patterns of individual response are complex and differ from patient to patient. Similarly, AI can improve the cutting-edge practice of medicine in various situations where identifying illness, making prognoses, or suggesting treatment depends on complex networks of factors not easily captured in explicit practice guidelines or other sources of medical knowledge. Medical AI could also push frontiers of explicit knowledge by suggesting new hypotheses and avenues of research, whether about medical care pathways or about the development of new pharmaceuticals.

Replicating and democratizing expertise

Despite the glamour of pushing medical frontiers, much work in medical AI has focused on replicating existing human capacity, such as AI that identifies malignant skin lesions from skin images as well as dermatologists. The IDx-DR device exemplifies this pattern. Approved by the FDA in May 2018 for autonomous diagnosis of diabetic retinopathy, IDx-DR consists of an automated retinal fundus imaging camera combined with two AI algorithms. A minimally trained operator uses the camera to acquire retinal fundus images for a diabetic patient; these images are sent to a first algorithm that evaluates image quality; if the quality is adequate, the images are evaluated by a second algorithm to determine whether the patient has more-than-mild diabetic retinopathy, indicating a need for further care. IDx-DR can allow a primary-care physician—or other provider—to provide screening care that would otherwise require an ophthalmologist.

This role for AI is most connected to worries about physician replacement. But we are far from that possibility. A much closer possibility is the use of medical AI to replicate capabilities that are now limited to relatively small numbers of specialists—and thus, to make those capabilities available to a much broader set of patients. Rather than AI replacing physicians who are present, AI could help non-specialists provide patients with care they might not otherwise be able to access.

Hundreds of millions of patients across the world lack meaningful access to even primary care; billions lack access to specialists of various kinds. AI will not replace ophthalmologists, but it could allow patients without ready access to ophthalmologists to get screenings they would otherwise not receive. Google is pursuing the possibility of deploying such ophthalmological AI in drastically underserved populations in rural India. Similarly, AI-powered screening of potentially malignant skin lesions via smartphone cameras will not replace existing dermatologists, but could help patients who lack ready access know that they need to seek out care. If AI can be deployed to increase the capacity of whatever providers are present—or to allow patients access to the technology on their own—it could radically improve the care available, especially to underserved patient populations.

Automating drudgery

AI can also transform medical practice in a more quotidian way, by automating medical drudgery. Some of this ability has been recognized; for instance, AI can triage radiologic images and flag those that demand closer attention, freeing physician time.

But AI could also make a difference in a broad set of time-consuming tasks: paperwork. Too much of physicians’ time is occupied by routine data search, data entry, and other desk work. According to one study, physicians spend nearly half of their time overall on desk work and electronic health record work; even in the examination room, about a third of physician time is spent on similar tasks. AI has the potential to automate at least some of this work, using natural language processing to identify the most relevant information from patient history or useful articles and resources from the medical literature. Even nearer-term, AI-driven speech recognition and natural language processing could take notes on patient encounters, structure the data, and flag the most relevant points. These interventions, while not exciting on their face, could free substantial physician time to interact more with patients, improving the practice of medicine and decreasing the leading cause of physician burnout. They could also enhance accuracy and patient privacy by automating the otherwise error-prone process of entering structured data or transcribing notes.

Allocating resources

Finally, AI offers the possibility of helping allocate resources efficiently across the medical system. Rather than freeing up provider time, or extending or replicating provider capabilities, AI can direct existing scarce resources where they are likely to do the most good. For instance, a resource-triage AI could help allocate scarce inpatient hospital beds when patient demand exceeds availability; similarly, AI could help allocate drugs during shortages (both issues with ethical and legal implications). More simply, AI could help optimize staffing to better match patterns of patient need. While these uses of AI raise ethical questions—who deserves resources, and what is to prevent administrators from using AI to maximize revenue rather than effectiveness of care?—resource allocation AI is nevertheless likely to become a pervasive, behind-the-scenes aspect of the medical system.

Conclusion

Medical AI will not replace physicians, at least not in the foreseeable future. But it will dramatically shift the shape of medical practice. When developers, providers, patients, policymakers, and regulators are debating how AI should be developed, regulated, and brought into practice, it is important to be clear about not only what medical AI can do and how it does it, but exactly what role it will play in the medical system to come.

W. Nicholson Price II is Assistant Professor at the University of Michigan Law School. You can reach him by e-mail at wnp at umich.edu

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