CMS Establishes New Billing Code for AI-Driven Calcium Analysis on CT Scans

Why It Matters

By linking reimbursement to FDA‑cleared AI analysis, CMS removes a key barrier, accelerating adoption of automated calcium quantification across routine imaging. This promises earlier cardiovascular risk detection and new revenue streams for health systems.

Key Takeaways

• CMS adds national billing code for AI CAC/AVC analysis effective April 1, 2026.
• Bunkerhill’s Contrast CAC and AVC receive FDA clearance, first for contrast CT.
• Carebricks platform automates referrals and follow‑up via AI agents.
• New Technology APC pathway provides clear reimbursement for AI cardiovascular tools.
• Multi‑institution consortium validates algorithms across diverse health system settings.

Pulse Analysis

Reimbursement has long been the linchpin for scaling artificial‑intelligence tools in radiology. CMS’s decision to create a dedicated billing code for AI‑driven calcium analysis signals a shift from experimental pilots to mainstream clinical practice. By embedding the code within the Hospital Outpatient Prospective Payment System, the agency offers a predictable payment structure that aligns with existing outpatient workflows, encouraging hospitals to invest in AI solutions without fearing revenue uncertainty.

Bunkerhill Health’s FDA‑cleared Contrast CAC and Contrast AVC algorithms expand the diagnostic reach of routine chest CTs. Unlike earlier AI tools limited to non‑contrast scans, these models extract calcium scores from contrast‑enhanced, non‑gated studies that are already performed for a wide array of indications. This capability turns every chest CT into a dual‑purpose exam, delivering actionable cardiovascular risk data without additional radiation or cost. Clinicians can now identify patients at elevated risk for coronary artery disease or aortic valve disease earlier, potentially prompting preventive interventions.

The integration of these algorithms into the Carebricks platform illustrates how AI can move beyond detection to execution. Carebricks leverages large language models and FDA‑cleared analytics to generate care pathways, automatically scheduling cardiology referrals, ordering follow‑up imaging, and notifying providers. The rapid, low‑code deployment model reduces implementation timelines from months to days, making it feasible for health systems of any size. As reimbursement aligns with technology, the market is poised for broader AI adoption, driving both improved patient outcomes and new revenue opportunities.