AI Platform (AIP001)

K232501

Exo Inc · cleared 2023-11-17 · product code QIH · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.5)
“Exo AI Platform is a software as a medical device (SaMD) that helps qualified users with image-based assessment of ultrasound examinations in adult patients.”
AlgorithmUltrasound image processing software implementing artificial intelligence including non-adaptive machine learning algorithms trained with clinical data intended for non-invasive analysis of ultrasound data and Deep Convolutional Neural Networks for Segmentation or Landmark Detection
source quote (p.6)
“Ultrasound image processing software implementing artificial intelligence including non-adaptive machine learning algorithms trained with clinical data intended for non-invasive analysis of ultrasound data Deep Convolutional Neural Networks for Segmentation or Landmark Detection”
Adaptive (vs locked)No
source quote (p.6)
“non-adaptive machine learning algorithms”
PCCPNo
Cybersecurity addressedFDA source did not state this

Validation studies (2)

Retrospective clinical

n=151 patients

endpoints: intraclass correlation coefficient (ICC); ejection fraction root mean square difference (RMSD)

standards: IEC 62304:2006/AC:2015 Medical device software Software life cycle processes, FDA's 'Content of Premarket Submissions for Device Software Functions" Guidance for Industry and Food and Drug Administration Staff Document issued on June 14, 2023, FDA Guidance (June 2022) “Technical performance assessment of quantitative imaging in radiological device premarket submissions”

Retrospective clinical

n=125 patients

endpoints: agreement using Cohen's kappa coefficient (к); intraclass correlation coefficient (ICC)

standards: IEC 62304:2006/AC:2015 Medical device software Software life cycle processes, FDA's 'Content of Premarket Submissions for Device Software Functions" Guidance for Industry and Food and Drug Administration Staff Document issued on June 14, 2023, FDA Guidance (June 2022) “Technical performance assessment of quantitative imaging in radiological device premarket submissions”

Reported performance (3 observations)

agreement_kappaas written: “ICC”0.93CI 0.91 – 0.95
source quote (p.8)
“All 0.93 (0.91 – 0.95)”
agreement_kappaas written: “Kappa”0.84
source quote (p.8)
“Kappa = 0.84”
agreement_kappaas written: “ICC”0.97
source quote (p.8)
“ICC = 0.97”

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
3
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/K232501