SwiftMR

AI-assisted MRI

Illustration of an MRI ring as a turquoise wire mesh, with a clock and a microscope inside the opening, without text

SwiftMR is AI-assisted software for improving MRI images after acquisition [1][2]. It can reduce noise and support image appearance. It does not replace the examination or a medical diagnosis. We use it across our exam types when appropriate and plan the approach together with you.

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What happens technically?

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  1. 01MRI acquisition
  2. 02SwiftMR image processing
  3. 03Physician assessment

MRI acquisition takes place on the scanner. SwiftMR can then process the resulting images [1][3]. The aim is clearer depiction and/or — with suitably adapted protocols — a shorter acquisition [4][5][6]. Medical interpretation remains with the reporting physicians.

Possible benefits

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  • Shorter scan time, less time lying still
  • Potentially more comfortable with pain, restlessness or enclosed-space discomfort
  • At the same or similar acquisition time: less image noise and clearer depiction of fine anatomical detail may be possible — without a guarantee in every case [1][4][5][6]

Total appointment time including preparation may differ from pure measurement time.

Shared decision-making

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In the consultation we clarify whether standard MRI or an AI-assisted approach is suitable for you. We take into account the clinical question, suitability and your preferences — for example whether a shorter acquisition matters most, or the clearest possible image quality.

Limits and risks

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As with any AI image processing, images may look smoother; fine detail can change or become harder to see [5][7]. In theory, unusual or false-appearing structures can arise, or existing detail can be lost; no established frequency of such effects is known for SwiftMR [7].

We store original acquired images and the processed results [2]. With accelerated protocols, original acquired images can be degraded so that they do not form a reliable diagnostic fallback. Unclear findings may require additional or longer scans. Diagnosis remains a physician responsibility.

Common questions

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Can I choose between standard MRI and AI?

Yes — we discuss the options together and choose what fits your clinical question and situation.

Does the AI diagnose me?

No. SwiftMR processes images; physicians interpret them.

Will the exam be shorter — or the image quality better?

Either can be the goal, depending on the situation. There is no guarantee of shorter overall times or better images in every case. Sometimes shorter acquisition comes first; sometimes clearer depiction at similar time.

What are the risks — and what about the original acquired images?

Possible effects include smoothing, altered fine detail and — in theory — false-appearing structures or loss of detail; no established frequency is known for SwiftMR. Original acquired images are stored but can be degraded after accelerated protocols and then may not be a reliable fallback. Further scans may be needed if findings are unclear.

Does this apply to all exams?

SwiftMR can be used across our exam types. Whether and how is decided individually with you.

Sources

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Sources [1] and [2] are from the manufacturer and the U.S. FDA. They are not a German regulatory clearance and do not prove clinical superiority. According to the manufacturer, SwiftMR carries a CE mark under the EU Medical Device Regulation (MDR) [8]. That is not a claim of clinical superiority. Several clinical studies ([4]–[6]) received manufacturer support. Source [3] is a preprint and not peer-reviewed. Source [7] is a general review of commercial AI products for MRI acceleration, not SwiftMR-specific.

  1. 1. AIRS Medical. SwiftMR product page. https://airsmed.com/swiftmr/
  2. 2. U.S. Food and Drug Administration. 510(k) Summary, SwiftMR, K230854. https://www.accessdata.fda.gov/cdrh_docs/pdf23/K230854.pdf
  3. 3. Jeong G, Kim H, Yang J, Jang K, Kim J. All-in-One Deep Learning Framework for MR Image Reconstruction. arXiv:2405.03684 [preprint]. https://arxiv.org/abs/2405.03684
  4. 4. Yoo H, Yoo RE, Choi SH, et al. Deep learning–based reconstruction for acceleration of lumbar spine MRI: a prospective comparison with standard MRI. Eur Radiol. 2023;33(12):8656–8668. https://doi.org/10.1007/s00330-023-09918-0
  5. 5. Lee J, Jung M, Park J, et al. Highly accelerated knee magnetic resonance imaging using deep neural network (DNN)-based reconstruction: prospective, multi-reader, multi-vendor study. Sci Rep. 2023;13:17264. https://doi.org/10.1038/s41598-023-44248-7
  6. 6. Choi KS, Park C, Lee JY, et al. Prospective Evaluation of Accelerated Brain MRI Using Deep Learning-Based Reconstruction: Simultaneous Application to 2D Spin-Echo and 3D Gradient-Echo Sequences. Korean J Radiol. 2025;26(1):54–64. https://doi.org/10.3348/kjr.2024.0653
  7. 7. Fransen SJ, Roest C, Simonis FFJ, Yakar D, Kwee TC. The scientific evidence of commercial AI products for MRI acceleration: a systematic review. Eur Radiol. 2025;35(8):4736–4746. https://doi.org/10.1007/s00330-025-11423-5
  8. 8. AIRS Medical. AIRS Medical Earns Expanded MDR Certification for AI-Powered MRI Image Enhancement Solution SwiftMR. 8 July 2025. https://airsmed.com/en/mdr-certification-swiftmr/
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