Imaging demand is growing faster than the workforce that reads it. AI can help, but only when it works inside the reading workflow.
annual U.S. deaths associated with diagnostic error, as cited from the National Academy of Medicine.
possible radiologist shortfall by 2033, as projected by the AAMC.
yearly growth in imaging volume, as cited from the American College of Radiology.
of physician time spent on administrative burden, as cited in the healthcare literature.
Figures are reported by the organizations named. See their publications for definitions, dates and methods.
Results that live in a separate viewer are rarely used. They need to appear in the study.
Performance on a vendor dataset is not performance on your scanners.
Independent and mid-size groups often lack in-house teams for this work, and enterprise platforms are not built for them.
Federal policy has treated artificial intelligence and health information technology as national priorities, including the National AI Initiative Act of 2020, the January 2025 executive order on AI, and the Federal Health IT Strategic Plan 2024–2030 from ONC and HHS.
RASPilot supports that direction in a practical way: making AI usable in everyday radiology, serving imaging providers across the United States.
Share your modalities, PACS and goals. We will reply with how an implementation could look for your group.
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