Change Healthcare Anatomical AI
K210719Change Healthcare Canada Company · cleared 2021-07-20 · product code QIH · Radiology
Premarket evidence — what FDA accepted
source quote (p.6)
“Change Healthcare Anatomical AI is a standalone image processing software application that analyzes CT and MR DICOM images to associate anatomic regions with images and exports the derived information for use in integrated healthcare systems.”
source quote (p.8)
“Machine learning based algorithm (non-adaptive)”
source quote (p.8)
“Machine learning based algorithm (non-adaptive)”
source quote (p.9)
“Change Healthcare Anatomical AI adheres to the cybersecurity requirements as defined by the FDA Guidance “Content of Premarket Submissions for Management for Cybersecurity in Medical Devices,” by implementing cybersecurity controls to protect data in use, in transit or at rest for the components in the product.”
Validation studies (1)
Retrospective clinical
sample size not stated · 27 site(s)
endpoints: accuracy results were evaluated according to patient demographics, healthcare institution, and other confounding imaging factors such as scanner manufacturer, presence of contrast, slice thickness, MR sequence, and CT kernel.
standards: ISO 14971:2007 – Medical devices – Application of risk management to medical devices, ISO 15223-1:2016 - Medical devices – Symbols to be used with medical devices labels, labeling, and information to be supplied – Part 1: General requirements, IEC 62304:2015 – Medical device software – Software life cycle processes, IEC 62366-1:2015 Medical devices – Part 1: Application of usability engineering to medical devices, NEMA PS 3.1-3.20 (2016) – Digital Imaging and Communications in Medicine (DICOM) set
Reported performance (0 observations)
FDA source did not state a quantitative performance metric — non-reporting is itself the signal.
Each value carries its own analysis unit and task — never compare or pool across devices. Source: 510(k) summary PDF.
Predicate network
Postmarket — what happened after clearance
Recall and MAUDE counts are product-code-level (reports aren't reliably attributable to one device). Signals are descriptive observables with sources — never a judgment that the device is unsafe or drifting. Snapshot 2026-07-08.
Reimbursement — how devices like this got paid
Not yet tracked — no payment pathway indexed for this clearance (the reimbursement corpus is a growing seed set).