MAGNETOM Vida; MAGNETOM Lumina; MAGNETOM Aera; MAGNETOM Skyra; MAGNETOM Prisma; MAGNETOM Prisma fit
K231560Siemens Medical Solutions USA, Inc. · cleared 2023-10-23 · product code LNH · Radiology
Premarket evidence — what FDA accepted
source quote (p.4)
“The MAGNETOM system is indicated for use as a magnetic resonance diagnostic device (MRDD) that produces transverse, sagittal, coronal and oblique cross-sectional images, spectroscopic images and/or spectra, and that displays the internal structure and/or function of the head, body, or extremities. Other physical parameters derived from the images and/or spectra may also be produced. Depending on the region of interest, contrast agents may be used. These images and/or spectra and the physical parameters derived from the images and/or spectra when interpreted by a trained physician yield information that may assist in diagnosis. The MAGNETOM system may also be used for imaging during interventional procedures when performed with MR compatible devices such as in-room displays and MR Safe biopsy needles. AI Features/Applications training and validation: The information below shows an executive summary of training and validation dataset of the AI features:”
source quote (p.11)
“AI Features/Applications training and validation: The information below shows an executive summary of training and validation dataset of the AI features: Deep Resolve Boost: ... Deep Resolve Sharp: ... [1] Bae SH et al., Clinical feasibility of accelerated diffusion weighted imaging of the abdomen with deep learning reconstruction: Comparison with conventional diffusion weighted imaging, Eur J Radiol., 154 (2022)”
Validation studies (8)
Bench
sample size not stated
Bench
sample size not stated
Bench
sample size not stated
Bench
sample size not stated
endpoints: SNR and image uniformity measurements for coils; Heating measurements for coils
Retrospective clinical
n=25,000 other
endpoints: peak signal-to-noise ratio (PSNR); structural similarity index (SSIM); aliasing artifacts; image sharpness; denoising levels
Retrospective clinical
n=10,000 other
endpoints: peak signal-to-noise ratio (PSNR); structural similarity index (SSIM); aliasing artifacts; image sharpness; denoising levels
Retrospective clinical
n=1,000,000 other
endpoints: peak signal-to-noise ratio (PSNR); structural similarity index (SSIM); aliasing artifacts; image sharpness; denoising levels
Retrospective clinical
n=10,000 images
endpoints: peak signal-to-noise ratio (PSNR); structural similarity index (SSIM); perceptual loss; visual rating; image sharpness by intensity profile comparisons
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
- recall_reason_pattern
Software/algorithm-related recall in product code LNH (Philips North America, initiated 2026-04-14): "The potential for stiffness value errors when a specific range of image reconstruction parameters is used in combination with Resoundant's algorithm, leading to the reconstruction " Recalling firm is another firm in the same product code.
first seen 2026-07-08 · recall res_event_number:98779
- recall_reason_pattern
Software/algorithm-related recall in product code LNH (Philips North America, initiated 2025-12-03): "The potential for stiffness value errors when viewing exported MR Elastography (MRE) stiffness maps to viewer Picture Archiving and Communication System (PACS)." Recalling firm is another firm in the same product code.
first seen 2026-07-08 · recall res_event_number:98111
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).