United States AI in Medical Imaging Market Outlook to 2030: Size, Share, Growth and Trends

The United States AI in Medical Imaging Market is valued at USD 548 Mn in 2024 on an industry revenue basis. That figure includes software platforms, AI-enabled hardware modules, and professional or managed services sold by AI medical imaging solution providers and OEMs to U.S. healthcare end-users. It does not represent imaging hardware revenue broadly, hospital radiology spend overall, or GMV-like transaction values. Commercially, this matters because the market is already large enough to support scaled vendors, but still early enough for rapid share gains through platform bundling, enterprise rollout, and modality expansion.

The United States AI in Medical Imaging Market is expected to grow from USD 548 Mn in 2024 to about USD 3,080 Mn by 2030, implying a 33.4% CAGR during 2025-2030. That is materially faster than the already strong 26.8% CAGR recorded during 2019-2024. The acceleration is driven by wider enterprise deployment, increasing cloud-based orchestration, and stronger clinical monetization in stroke, CT, and breast screening use cases. The forecast also assumes that health systems continue moving from single-algorithm pilots toward multi-module contracts with integration and managed-service components.

The next profit pool shift is from stand-alone modality algorithms toward application-layer and enterprise orchestration revenue. CT remains the largest modality pool at USD 191 Mn in 2024, but the fastest growth is at about 38% CAGR, and in broader workflow layers that sit across multiple modalities and departments. This is important because integrated platforms capture implementation fees, recurring subscriptions, governance services, and cross-sell opportunities that point solutions typically cannot. Over time, that should improve retention and expand lifetime revenue per health-system account.

The main risks are reimbursement selectivity, evidence burden, and workflow fragmentation across provider environments. Commercial adoption is easier when AI ties directly to high-acuity care economics, but many use cases still depend on indirect ROI rather than explicit reimbursement. At the same time, FDA and ONC expectations around transparency, lifecycle control, and interoperable decision support raise compliance and integration costs. Finally, provider IT maturity varies widely across the U.S. hospital base, which can slow deployment outside leading integrated systems. These factors do not stop growth, but they do favor vendors with deeper regulatory, clinical, and implementation capabilities.

The United States AI in Medical Imaging Market is the largest and fastest-growing market among the selected peer set of Canada, Germany, the United Kingdom, France, and Japan. Its advantage comes from heavier advanced-imaging utilization, a larger hospital base, deeper FDA-cleared radiology AI activity, and greater availability of scaled commercial buyers such as integrated delivery networks and national radiology groups. In contrast, most peer markets benefit from centralized public screening programs and strong hospital digitization, but they tend to commercialize more slowly because procurement is more centralized and reimbursement pathways are narrower.

The strongest long-term demand drivers are imaging intensity, acute neurological burden, and oncology or screening volume. The United States already operates one of the most imaging-intensive systems globally, which makes even small workflow improvements economically meaningful. Stroke care is especially valuable because treatment timing has direct clinical and financial consequences, while oncology and breast screening create repeat-use environments for detection, quantification, and follow-up tools. These demand anchors are stronger than temporary technology hype because they arise from permanent care-pathway pressure inside large hospital networks and imaging groups.

CT Scan AI Solutions are the dominant segment in the United States AI in Medical Imaging Market, generating USD 191 Mn in 2024, or 34.9% of total market value. CT leads because it sits at the center of emergency, stroke, trauma, chest, and abdominal imaging, where turnaround time has measurable operational and clinical value. CT also benefits from easier ROI articulation than lower-volume modalities because hospitals can quantify improvements in prioritization, workflow routing, and acute finding detection. That makes CT the most defensible entry point for both OEM-bundled and independent AI vendors.