AIRS Medical vs Lunit
Two Korean imaging AI companies doing opposite things to the same department's workload. AIRS changes the image, using deep learning reconstruction so a scanner can work faster or at lower dose, vendor neutral across manufacturers and routing the original series to a separate archive so a reader can check what changed. Lunit finds cancer on chest radiographs and mammograms, with the deeper evidence base in screening populations where specificity multiplies across a large healthy denominator. One returns scanner capacity, the other returns findings. The failure asymmetry is worth holding: a detector that degrades stops flagging and a quality process can notice, while a reconstruction that degrades produces a cleaner image that quietly lost something.
- It changes how the image looks rather than what is found in it, cutting scan time or raising quality on scanners the department already owns.
- Deliberate vendor neutrality across scanner manufacturers matters for a mixed fleet, and the cleared scope covers all pulse sequences within its modality.
- Original and processed series route to separate archives independently, so the unmodified study survives for checking.
- Cancer detection across chest radiograph and mammography, with a listed parent and regulatory footprints in multiple markets.
- The evidence base in screening populations is deeper than most detection vendors, which is where specificity matters most.
- For a screening programme, a vendor built around cancer detection is the closer fit.
Side by Side
| Axis | A AIRS Medical |
L Lunit |
|---|---|---|
| AI Centrality | ||
| Autonomy and Oversight Model | ||
| Model and Technology Transparency | ||
| Clinical and Operational Evidence | ||
| AI Safety and PHI Stewardship | ||
| HIPAA and BAA Posture | ||
| Security Certifications and Trust Center | ||
| FDA and Regulatory Status | ||
| AI Governance and Bias Disclosure | ||
| EHR and Interoperability Depth | ||
| Deployment Model and Data Residency | ||
| Commercial Transparency | ||
| Setting and Specialty Coverage |
Related comparisons
Other published head to head assessments involving these vendors or their closest peers. The full set for this category is on the Radiology & Imaging AI page.
These do opposite jobs and their failure modes differ in visibility: a detection model that degrades quietly stops flagging, which a quality process can catch, while a reconstruction model that degrades produces a cleaner image that lost something no reader will notice. That asymmetry is why retaining the original series matters more than any accuracy figure on the enhancement side. Neither publishes subgroup performance. Neither publishes pricing, and the economics differ, since one adds to the read and the other returns scanner capacity.