Artificial intelligence (A.I.) technologies are increasingly deployed across clinical care, yet reimbursement remains a major barrier to sustainable adoption. The Centers for Medicare & Medicaid Services (CMS) currently reimburses A.I.-enabled technologies through a fragmented set of procedural codes, add-on payments, and legacy payment models, none of which were designed to support the complexity or workflow integration of clinical A.I. This article examines how existing CMS reimbursement pathways for A.I. function in practice, identifies structural misalignments that limit adoption, and highlights the risks of continuing to rely on these approaches. We then propose policy-level solutions to modernize A.I. reimbursement, including clearer coverage pathways, value-aligned payment models, and mechanisms to promote equitable adoption across diverse healthcare settings. Aligning reimbursement with clinical value is essential to ensure that A.I. improves care delivery and can be sustainably integrated into routine clinical practice.
Bains et al. (Tue,) studied this question.