KEY POINTS Question: How has billed use of artificial intelligence (AI)-related CPT codes changed nationally since 2021, and how does use differ by patient, geographic, and payer characteristics? Findings: In this cross-sectional study of 11.6 billion US encounters, 2.9 per 100,000 included an AI-required CPT code. Odds of use were nearly 10 times higher in 2025 than in 2021, higher among Medicare beneficiaries, and lower among Medicaid enrollees and residents of socially vulnerable areas. Meaning: Billed AI use remains rare and uneven across payers and communities, so payment policy for AI may need to be paired with monitoring of who receives AI-enabled care. ABSTRACT Importance: Payment policy for clinical artificial intelligence (AI) is changing quickly, yet national data on which AI-enabled services are used, and for whom, are limited. Objective: To describe national trends in the use of AI-related Current Procedural Terminology (CPT) codes and how use differs by patient, geographic, and payer characteristics. Design, Setting, and Participants: This serial cross-sectional study used Epic Cosmos, a deidentified electronic health record dataset, to examine 11,605,867,309 encounters among 233,628,292 US patients from January 1, 2021, to April 14, 2026. Exposures: Calendar year, age, sex, race, ethnicity, rurality, area-level social vulnerability, census region, and primary payer. Main Outcomes and Measures: Encounters with any of 65 CPT codes that require AI, identified by matching the CPT code set to US Food and Drug Administration-listed AI-enabled devices and classified as assistive, augmentative, or autonomous using the CPT Appendix S taxonomy. Results: Of 11.6 billion encounters, 335,349 (2.9 per 100,000) included an AI-required CPT code. The odds of an encounter with an AI-required code were 9.88 (95% CI, 9.71-10.06) times higher in 2025 than in 2021. In unadjusted comparisons, use was higher among patients aged 65 to 85 years and among those with Medicare (OR, 5.11) or Medicare Advantage (OR, 5.12) vs commercial insurance. Use was lower among Black (OR, 0.72) and Hispanic or Latino (OR, 0.34) patients, Medicaid enrollees (OR, 0.65), residents of the most socially vulnerable areas (OR, 0.53), and residents of the most rural areas (OR, 0.86). Use of automated diabetic retinopathy screening increased after the service was added to a HEDIS quality measure. One newer code (quantitative brain MRI) appeared in rural areas within months of becoming effective, whereas older codes such as retinopathy screening took years. Conclusions and Relevance: In this cross-sectional study, billed use of AI-enabled services remained rare but grew substantially and was concentrated among older and Medicare-insured patients, with lower use among Medicaid enrollees and residents of socially vulnerable areas. As the Centers for Medicare & Medicaid Services restructures payment for software and launches outcome-based models, tracking who receives AI-enabled care across payment pathways will be important for equitable access.