Catalyzing Health AI by Fixing Payment Systems

Abstract

Despite rapid advances in artificial intelligence (AI) across sectors, health care remains one of the least transformed domains. This stagnation is not due to lack of data, clinical need, or innovation, but rather to persistent regulatory and economic misalignment. Even AI tools cleared by the U.S. Food and Drug Administration that meet clinical efficacy standards often face major barriers to adoption, largely driven by outdated reimbursement frameworks and fragmented incentives among stakeholders. The result is a systemic failure to deploy technologies that could meaningfully reduce clinician workload, shorten wait times, and improve patient lives. In this article, we examine the reimbursement landscape for health AI, focusing first on tools that fit existing regulatory pathways, outlining payment barriers and proposing policy reforms. These include resolving Current Procedural Terminology adoption bottlenecks, addressing integration overhead, and aligning pricing models with AI cost structures. We then extend the discussion to the emerging domain of generative AI in health care, highlighting the urgent need for prospective regulatory frameworks to ensure patient benefits. (Funded by the National Institutes of Health and the Leukemia and Lymphoma Society.).

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