AIRS Medical

Deep learning is the whole product. The company sells no scanner and no hardware; SwiftMR applies a model after image acquisition to remove noise and blur, which is what allows either shorter scans or better images from the same acquisition. The vendor neutral design reinforces this, since the value proposition is explicitly the algorithm rather than any imaging system it runs behind.

The system operates automatically in the background with images reaching PACS typically in under two minutes, and the radiologist reads the enhanced image rather than reviewing an AI finding. That places the oversight question in an unusual and important spot: this AI does not detect anything, it alters the image the diagnosis is made from. The FDA clearance is what governs whether that alteration preserves diagnostic quality, which makes the regulatory pathway the substantive safety control here rather than clinician review.

The method is described concretely, including that enhancement is applied post acquisition without altering the scan itself, and that the newest capability was achieved by training specifically on images already processed by scanner manufacturers' own deep learning pipelines and testing across multiple vendors and field strengths. That is a specific and checkable technical account. The company also publishes candid qualifiers that scan time results vary by scanner model, field strength, protocol, and site configuration. Model architecture and quantitative image quality metrics are not published.

Evidence is operational and reasonably specific, with deployment scale as the strongest signal: a reported 1,700 plus imaging centers and hospitals across more than 40 countries, in clinical use since 2023. Named site results include routine brain scans falling from 15 to 9 minutes on a scanner already running the manufacturer's own deep learning reconstruction, and a site reporting appointments cut from 30 to 20 minutes. The company appropriately labels these as individual site results that may not be typical. What is absent is published reader study evidence that diagnostic accuracy is preserved at reduced scan times, which is the clinical question underneath the operational one.

No published PHI handling or data governance disclosure was located. The processing pipeline moves patient imaging out of the scanner environment and back into PACS, so a buyer should establish where inference runs and what is retained.

No HIPAA or BAA commitment was located in public materials. The company operates globally across more than 40 countries, so a United States buyer should confirm both HIPAA standing and where processing occurs relative to the company's Korean base.

No SOC 2, ISO 27001, or equivalent attestation was located, and no trust center was found.

FDA 510(k) cleared with a clearly stated scope: image enhancement supporting scan time reduction of up to 50 percent, covering all pulse sequences and body parts, in clinical use since 2023. The company subsequently obtained an additional clearance in April 2026 specifically permitting operation in conjunction with scanner manufacturers' own deep learning reconstruction pipelines, which is a meaningful and unusual regulatory step because it addresses the stacked processing case rather than assuming it. Buyers should note the clearance covers image enhancement and scan time reduction; it is not a detection or diagnostic claim.

No governance framework or bias evaluation was located. The relevant question for image enhancement is whether reconstruction quality varies by patient body habitus, pathology type, or rare findings, since a model trained toward typical anatomy could in principle smooth away the unusual, and no published analysis addresses that.

Interoperability here is the imaging stack rather than the chart, and vendor neutrality is the design commitment: the platform works across scanner manufacturers, field strengths, and body parts, returns enhanced images into the customer's PACS typically within two minutes, and now holds clearance to operate alongside OEM deep learning reconstruction rather than requiring the customer to choose between them. For a radiology department running mixed scanner fleets that is the difference between one deployment and several. No EHR integration applies or is claimed.

No hosting, tenancy, or data residency terms were located. Sub two minute turnaround to PACS implies either cloud inference or an on premise appliance, and which of those applies is a material question for a buyer given cross border considerations.

No published pricing. The company frames value in throughput terms, citing a site generating more than 100,000 dollars in additional monthly patient revenue from added scan capacity, which lets a buyer model return but not cost.

Single modality by design, which is MRI, but broad within it: the cleared scope spans all pulse sequences and all body parts across vendors and field strengths, which is wider than most imaging AI that is cleared organ by organ. Settings span imaging centers and hospitals, with a second product referenced for the imaging portfolio. Buyers outside MRI are outside scope.