RevealAI-Lung

K251769

Precision Medical Ventures, Inc. Dba Revealdx · cleared 2026-01-30 · product code POK · Radiology

Premarket evidence — what FDA accepted

Device typesamd

source quote (p.7)

“RevealAI-Lung is a software only device.”

Algorithmmachine learning algorithm trained on features calculated from nodules

source quote (p.6)

“The mSI value is expressed as a numeric value from 0 to 1 based on a machine learning algorithm trained on features calculated from nodules in the NLST database where diagnoses were confirmed.”

Adaptive (vs locked)FDA source did not state this

PCCPFDA source did not state this

Cybersecurity addressedYes

source quote (p.15)

“Cybersecurity activities were performed using FDA’s “Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions; Guidance for Industry and Food and Drug Administration Staff” (Document issued on September 27, 2023)”

Validation studies (2)

Reader study (MRMC)

n=108 cases · 4 site(s)

endpoints: discrimination was measured using the area under the receiver operating characteristic curve (AUC) across all cases; consistency of device performance and reader interpretation, and sensitivity and specificity measures

Standalone

n=675 patients · 3 site(s)

endpoints: device performance against the ground truth; AUC > 0.8; follow-up decisions would be improved compared to clinical guidelines

standards: ISO 14971:2019

Reported performance (4 observations)

sensitivity0.82CI ± 0.036

source quote (p.24)

“Use of RevealAI-Lung increased sensitivity by 14 points (0.68 ± 0.039 to 0.82 ± 0.036)”

specificity0.467CI ± 0.043

source quote (p.24)

“and specificity by 12 points (0.344 ± 0.041 to 0.467 ± 0.043).”

aurocas written: “auc”0.719

source quote (p.23)

“Mean: 0.538 0.719 0.181”

aurocas written: “AUC improvement”0.181

source quote (p.23)

“This difference was statistically significant (p < 0.0001; Dorfman-Berbaum-Metz ANOVA random-reader random-case (RRRC) with jackknife (Wilcoxon)).”

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

recalls in product code, 24mo

MAUDE reports in code, 12mo

—

vs code's own 3-yr baseline

drift signals on this device

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).