Risks and remedies for artificial intelligence in health care

liu, tempo Date: 2021-09-26 10:24:44 From:brookings
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2021 medical AI:Artificial intelligence (AI) is rapidly entering health care and serving major roles, from automating drudgery and routine tasks in medical practice to managing patients and medical resources. As developers create AI systems to take on these tasks, several risks and challenges emerge, including the risk of injuries to patients from AI system errors, the risk to patient privacy of data acquisition and AI inference, and more.


Potential solutions are complex but involve investment in infrastructure for high-quality, representative data; collaborative oversight by both the Food and Drug Administration and other health-care actors; and changes to medical education that will prepare providers for shifting roles in an evolving system.



Although the field is quite young, AI has the potential to play at least four major roles in the health-care system:


Pushing boundaries of human performance. The flashiest use of medical AI is to do things that human providers—even excellent ones—cannot yet do. For instance, Google Health has developed a program that can predict the onset of acute kidney injury up to two days before the injury occurs; compare that to current medical practice, where the injury often isn’t noticed until after it happens.Such algorithms can improve care beyond the current boundaries of human performance.


AI systems


Democratizing medical knowledge and excellence. AI can also share the expertise and performance of specialists to supplement providers who might otherwise lack that expertise. Ophthalmology and radiology are popular targets, especially because AI image-analysis techniques have long been a focus of development.


Several programs use images of the human eye to give diagnoses that otherwise would require an ophthalmologist. Using these programs, general practitioner, technician, or even a patient can reach that conclusion.Such democratization matters because specialists, especially highly skilled experts, are relatively rare compared to need in many areas.


Automating drudgery in medical practice. AI can automate some of the computer tasks that take up much of medical practice today. Providers spend a tremendous amount of time dealing with electronic medical records, reading screens, and typing on keyboards, even in the exam room. If AI systems can queue up the most relevant information in patient records and then distill recordings of appointments and conversations down into structured data, they could save substantial time for providers and might increase the amount of facetime between providers and patients and the quality of the medical encounter for both.


Managing patients and medical resources. Finally, and least visibly to the public, AI can be used to allocate resources and shape business. For instance, AI systems might predict which departments are likely to need additional short-term staffing, suggest which of two patients might benefit most from scarce medical resources, or, more controversially, identify revenue-maximizing practices.



While AI offers a number of possible benefits, there also are several risks:


Injuries and error. The most obvious risk is that AI systems will sometimes be wrong, and that patient injury or other health-care problems may result. If an AI system recommends the wrong drug for a patient, fails to notice a tumor on a radiological scan, or allocates a hospital bed to one patient over another because it predicted wrongly which patient would benefit more, the patient could be injured. Of course, many injuries occur due to medical error in the health-care system today, even without the involvement of AI.


AI errors are potentially different for at least two reasons. First, patients and providers may react differently to injuries resulting from software than from human error. Second, if AI systems become widespread, an underlying problem in one AI system might result in injuries to thousands of patients—rather than the limited number of patients injured by any single provider’s error.

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