Jazz

K223659

AI Medical AG · cleared 2023-09-22 · product code LLZ · Radiology

FDA 510(k) summary for Jazz (K223659)

Constat organizes this clearance into predicate context, validation evidence, reported performance, postmarket signals, and reimbursement links. Each extracted claim stays tied to the public FDA source; missing fields remain explicitly unstated.

Validation studies
1
Cited predicates
1
Performance observations
4
Product code
LLZ
Official FDA 510(k) database record ↗FDA 510(k) source PDF ↗

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.5)
“Jazz is a Software as a Medical Device (SaMD) consisting of a software intended to be a facilitating tool for the physicians, in the sense of a semi-automatic pipeline for the process of identifying, labeling and quantifying the volume of segmentable brain structures identified on MR images.”
Algorithmmachine learning algorithms
source quote (p.7)
“This device uses machine learning algorithms as part of its key functionality. Networks were trained using brain images, which were fully segregated from the test set, and using a ground truth which was set using gold standard human expert opinion.”
Adaptive (vs locked)No
source quote (p.7)
“Networks were trained using brain images, which were fully segregated from the test set, and using a ground truth which was set using gold standard human expert opinion. “Semi-automatic" refers to the possibility given to the physician to correct the segmentation of the software before saving.”
PCCPNo
Cybersecurity addressedFDA source did not state this

Validation studies (1)

Retrospective clinical

n=344 patients

endpoints: voxel-wise sensitivity; voxel-wise specificity; lesion-wise dice score; lesion-wise true positive rate; lesion-wise false negative rate; lesion-wise false discovery rate; anatomy localization score; coregistration score; identical number of lesions; total lesions volume; report generated in a process-reprocess experiment

Reported performance (4 observations)

sensitivity0.4CI at least
source quote (p.7)
“In one accuracy experiment, the acceptance criteria for device performance were set to be a voxel-wise sensitivity of at least 40%, a voxel-wise specificity of at least 95%, and a lesion-wise dice score of at least 0.5 for both the multiple sclerosis high sensitivity and high specificity models, as well as a lesion-wise true positive rate of at least 60% and a lesion-wise false negative rate of at most 40% for the high sensitivity model, as well as a lesion-wise false discovery rate of at most 50% for the high specificity model.”
specificity0.95CI at least
source quote (p.7)
“In one accuracy experiment, the acceptance criteria for device performance were set to be a voxel-wise sensitivity of at least 40%, a voxel-wise specificity of at least 95%, and a lesion-wise dice score of at least 0.5 for both the multiple sclerosis high sensitivity and high specificity models, as well as a lesion-wise true positive rate of at least 60% and a lesion-wise false negative rate of at most 40% for the high sensitivity model, as well as a lesion-wise false discovery rate of at most 50% for the high specificity model.”
diceas written: “lesion-wise dice score”0.5CI at least
source quote (p.7)
“In one accuracy experiment, the acceptance criteria for device performance were set to be a voxel-wise sensitivity of at least 40%, a voxel-wise specificity of at least 95%, and a lesion-wise dice score of at least 0.5 for both the multiple sclerosis high sensitivity and high specificity models, as well as a lesion-wise true positive rate of at least 60% and a lesion-wise false negative rate of at most 40% for the high sensitivity model, as well as a lesion-wise false discovery rate of at most 50% for the high specificity model.”
sensitivityas written: “lesion-wise true positive rate”0.6CI at least
source quote (p.7)
“In one accuracy experiment, the acceptance criteria for device performance were set to be a voxel-wise sensitivity of at least 40%, a voxel-wise specificity of at least 95%, and a lesion-wise dice score of at least 0.5 for both the multiple sclerosis high sensitivity and high specificity models, as well as a lesion-wise true positive rate of at least 60% and a lesion-wise false negative rate of at most 40% for the high sensitivity model, as well as a lesion-wise false discovery rate of at most 50% for the high specificity model.”

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

25
recalls in product code, 24mo
397
MAUDE reports in code, 12mo
+963%
vs code's own 3-yr baseline
34
drift signals on this device
  • predicate_cohort_contagion

    K223659 shares predicate K192130 with K213706 (AI-Rad Companion Brain MR), whose product code QIH recorded 1 recall event(s) in the trailing 24 months.

    first seen 2026-09-02 · predicate:K192130 sibling:K213706

  • predicate_cohort_contagion

    K223659 shares predicate K192130 with K231929 (iQ-solutions), whose product code QIH recorded 1 recall event(s) in the trailing 24 months.

    first seen 2026-09-02 · predicate:K192130 sibling:K231929

  • predicate_cohort_contagion

    K223659 shares predicate K192130 with K232305 (AI-Rad Companion Brain MR), whose product code QIH recorded 1 recall event(s) in the trailing 24 months.

    first seen 2026-09-02 · predicate:K192130 sibling:K232305

  • predicate_cohort_contagion

    K223659 shares predicate K192130 with K253057 (AI-Rad Companion Brain MR), whose product code QIH recorded 1 recall event(s) in the trailing 24 months.

    first seen 2026-09-02 · predicate:K192130 sibling:K253057

  • recall_reason_pattern

    Software/algorithm-related recall in product code LLZ (Merge Healthcare, Inc., initiated 2026-07-22): "Software issue may cause measurements to be displayed inaccurately." Recalling firm is another firm in the same product code.

    first seen 2026-09-02 · recall res_event_number:99520

  • recall_reason_pattern

    Software/algorithm-related recall in product code LLZ (FUJIFILM Healthcare Americas Corporation, initiated 2026-07-22): "Software issue may cause the ruler measurement to be inaccurate." Recalling firm is another firm in the same product code.

    first seen 2026-09-02 · recall res_event_number:99529

  • …and 28 more.

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 2026-09-02.

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.

Related FDA clearances

More AI/ML devices under product code LLZ

These are recent clearances in the same FDA product-code category. Shared classification does not by itself make a device an appropriate predicate.

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Constat Precedent · public FDA/CMS data · descriptive decision-support, not regulatory or reimbursement advice. Share this page: constat.dev/precedent/device/K223659