Genius AI Detection 2.0

K243341

Hologic, Inc. · cleared 2025-07-31 · product code QDQ · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.5)
“Genius Al Detection 2.0 is a software device intended to identify potential abnormalities in breast tomosynthesis images.”
Algorithmdeep learning networks, machine learning
source quote (p.5)
“Genius Al Detection 2.0 analyzes each standard mammographic view in a digital breast tomosynthesis examination using deep learning networks.”
Adaptive (vs locked)FDA source did not state this
PCCPNo
Cybersecurity addressedNo

Validation studies (3)

Retrospective clinical

n=1,475 patients · 15 site(s)

endpoints: AUC of ROC curve; sensitivity; specificity; false marker rate per view; accuracy (non-inferiority)

standards: IEC 62304: 2015, ISO 14971: 2019

Retrospective clinical

n=1,475 patients · 15 site(s)

endpoints: accuracy of the CC-MLO correlation algorithm

standards: IEC 62304: 2015, ISO 14971: 2019

Retrospective clinical

n=480 patients

endpoints: location specific cancer detection sensitivity; specificity

standards: IEC 62304: 2015, ISO 14971: 2019

Reported performance (4 observations)

sensitivity0.76CI 68%~84%
source quote (p.10)
“The detection performance of GAID 2.0 measured on a set of 132 cancer patients and 348 negative subjects with implant displaced images demonstrated location specific cancer detection sensitivity of 76% (CI 68%~84%) and specificity of 67% (CI 62%~72%).”
specificity0.67CI 62%~72%
source quote (p.10)
“The detection performance of GAID 2.0 measured on a set of 132 cancer patients and 348 negative subjects with implant displaced images demonstrated location specific cancer detection sensitivity of 76% (CI 68%~84%) and specificity of 67% (CI 62%~72%).”
accuracyas written: “CC-MLO correlation accuracy (malignant lesions)”0.9
source quote (p.9)
“The CC-MLO Correlation algorithm accurately correlated the Genius Al Detection software 2.0 marks on 90% of the biopsy-proven malignant lesions.”
accuracyas written: “CC-MLO correlation accuracy (negative cases)”0.73
source quote (p.9)
“In addition, 73% of correlated pairs of marks on negative cases were considered as accurate by expert radiologists.”

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

0
recalls in product code, 24mo
0
MAUDE reports in code, 12mo
—
vs code's own 3-yr baseline
0
drift signals on this device

Recall and MAUDE counts are product-code-level (reports aren't reliably attributable to one device); a recall is shown as device-attributed only when the recall record itself lists this clearance number. 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).

Applicable FDA guidance — what the submission is measured against

FDA guidance documents and guiding principles applicable to 510(k) AI/ML devices in the Radiology panel. A curated reference index, not legal or regulatory advice — each item states its own status, and a draft is never binding.

Applicability is derived from the device's FDA advisory panel and pathway — cross-cutting guidances apply to every AI/ML device; panel-specific ones are flagged. Titles, dates, and links verified against fda.gov as of July 2026.

Constat Precedent · public FDA/CMS data · descriptive decision-support, not regulatory or reimbursement advice. Share this page: constat.dev/precedent/device/K243341