Micro C Medical Imaging System, M01

K212654

OXOS Medical, Inc. · cleared 2022-02-04 · product code IZL · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.9)
“AILARA is a static artificial intelligence (AI) based algorithm in the updated software.”
Algorithmstatic artificial intelligence (AI) based algorithm
source quote (p.9)
“AILARA is a static artificial intelligence (AI) based algorithm in the updated software.”
Adaptive (vs locked)No
source quote (p.9)
“AILARA is a static artificial intelligence (AI) based algorithm in the updated software.”
PCCPNo
Cybersecurity addressedNo

Validation studies (6)

Bench

sample size not stated

endpoints: mean squared error; mean absolute error

standards: FDA's Proposed Regulatory Framework for Modifications to Artificial intelligence/Machine Learning (AI/ML) Based Software as a Medical Device (SaMD)(2019)

Bench

sample size not stated

endpoints: software met system-level software requirements; software outputs met the expected result

standards: IEC 62304:2015, Medical device software - Software life cycle processes, ISO 14971:2019, Medical Devices – Application of Risk Management to Medical Devices

Bench

sample size not stated

endpoints: images were determined to be diagnostically and clinically relevant

Bench

sample size not stated

endpoints: acceptable amount of radiation was delivered; AILARA dose values were below the established Diagnostic Reference Levels

Bench

sample size not stated

endpoints: acceptable amount of radiation was delivered; AILARA dose values were below the established Diagnostic Reference Levels (DRLs) for small size anatomies

standards: Pediatric Information for X-ray Imaging Device Premarket Notifications (2017)

Bench

n=15 other

endpoints: acceptable use-related risks and effectiveness during use; identified critical use tasks were completed with a passing result by 100% of participants

standards: IEC 62366-1:2020, Medical devices - Part 1: Application of usability engineering to medical devices, Guidance for Industry and FDA Staff – Applying Human Factors and Usability Engineering to Medical Devices (2016)

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

58
recalls in product code, 24mo
25
MAUDE reports in code, 12mo
-48%
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/K212654