510(k) Predicate Finder
Find potential predicate devices for your 510(k) submission. Enter a K-number to find all clearances with the same product code, a three-letter product code to browse clearances directly, or search by device name.
Let the assistant find your predicates
Describe your device and the Research Assistant searches 510(k)s, compares intended use, and drafts a predicate shortlist — with citations.
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Radiological Software Device To Predict Future Breast Cancer Risk
Radiology
A radiological software device to predict future breast cancer risk is a radiology software device that analyzes breast imaging radiological images and/or derived inputs to provide qualified healthcare professionals with a numeric probability and/or risk category indicating the patient's future (incident) breast cancer risk from the time the analyzed images were acquired; it is not intended to diagnose, detect, or inform the treatment of existing cancer. It is classified as FDA Class II (510(k) required) under regulation 892.8500 in the Radiology specialty, with product code SEZ. The device is not an implant and does not support life-sustaining functions.
510(k) Clearances
1 matchesWhat is a Predicate Device?
A predicate device is a legally marketed device that a new 510(k) applicant uses to demonstrate substantial equivalence. The FDA's 510(k) pathway requires manufacturers to show that their device has the same intended use and similar technological characteristics as one or more predicate devices already cleared for commercial distribution.
Finding the right predicate is a critical step in 510(k) strategy. Manufacturers typically search for cleared devices with the same FDA product code, which groups devices by their intended use and regulatory classification. This tool helps you identify potential predicates by browsing all clearances within a product code.
Note that predicate devices must be legally marketed — they cannot be devices that were cleared but never commercialized, or devices subject to a mandatory recall. The determination of substantial equivalence considers intended use, technological characteristics, and performance data.