Regulation of AI in Healthcare
The paper illuminates the complexities of AI regulation in healthcare and then proposes guidelines that balance public protections with the need for healthcare innovation. Among the more noteworthy proposals is the recommendation that AI regulation must specify both the type of AI technology and the healthcare context to which each rule applies. In other words, rules would use granular descriptions like "artificial neural networks in medical image analysis" instead of vague language like "healthcare AI." This emphasis on the combination of technology definition and medical context recognizes that AI risk is closely tied to these two factors.
The paper also recommends that policymakers use existing regulatory agencies — which already have industry experience — to govern AI in healthcare instead of a centralized AI office or AI czar. The paper argues such centralization would decrease regulatory awareness of industry-specific considerations and risk duplicative rulemaking between the AI office and other government agencies. Instead, the guidelines advocate for regulatory agencies having the personnel, internal AI expertise, and resources necessary to perform their duties properly.
The report's guidelines also touch on matters such as:
- aligning regulatory efforts with the FDA's historic work of evaluating software as a medical device;
- handling scenarios where AI-enabled devices have empirically confirmed benefits but whose mechanism of effect is hard to explain; and
- concerns around algorithmic discrimination and data privacy in AI-enabled systems.
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SOURCE Paragon Health Institute
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