AIRS Medical vs Lunit (2026)

AI Health Index verifiedAugust 4, 2026
Verdict

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.

The case for AIRS Medical
  • 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.
The case for Lunit
  • 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.

This comparison is published by AI Health Index, an independent research platform that compares healthcare AI vendors objectively. AIRS Medical and Lunit are each graded against the same capability taxonomy, from each vendor's own public materials and the regulatory record, under the AI Health Index verification standard. No vendor pays for placement, and no vendor has reviewed this page. How this evidence is graded

At a Glance

Plain facts

Fact AIRS Medical Lunit
Primary category Radiology & Imaging AI Radiology & Imaging AI
Founded 2018 2013
Headquarters Seoul, South Korea Seoul, South Korea
Website airsmed.com lunit.io
Attribute Matrix

Side by Side

Axis
A
AIRS Medical
L
Lunit
AI Centrality
Autonomy and Oversight Model
Model and Technology Transparency
Model Supply Chain Disclosure
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
AI Liability and Recourse
EHR and Interoperability Depth
Deployment Model and Data Residency
Commercial Transparency
Setting and Specialty Coverage
Citable Summaries

Each record in one paragraph

Written to be quoted whole. Each paragraph states what the AI Health Index verified about the vendor, with the caveats attached. Generated from this pair’s live capability grades, so it moves when a grade moves.

AIRS Medical

The AI Health Index awards AIRS Medical its top capability grade on AI Centrality and FDA and Regulatory Status. Set against Lunit, AIRS Medical grades higher on several axes, including Model Supply Chain Disclosure, AI Safety and PHI Stewardship and Security Certifications and Trust Center. Grades reflect evidence the AI Health Index could verify at the last review, so a low grade records disclosure the vendor has not published rather than a capability it has been shown to lack.

Source: AI Health Index, August 2026

Lunit

The AI Health Index awards Lunit its top capability grade on several axes, including AI Centrality, Model and Technology Transparency and Clinical and Operational Evidence. Set against AIRS Medical, Lunit grades higher on several axes, including Model and Technology Transparency, Clinical and Operational Evidence and AI Governance and Bias Disclosure. Its thinnest published disclosure sits on Model Supply Chain Disclosure. Grades reflect evidence the AI Health Index could verify at the last review, so a low grade records disclosure the vendor has not published rather than a capability it has been shown to lack.

Source: AI Health Index, August 2026

FAQ

Questions buyers ask

Should we choose AIRS Medical or Lunit?

On the axes where the AI Health Index separates them, AIRS Medical grades higher on several axes, including Model Supply Chain Disclosure, AI Safety and PHI Stewardship and Security Certifications and Trust Center, and Lunit grades higher on several axes, including Model and Technology Transparency, Clinical and Operational Evidence and AI Governance and Bias Disclosure. Neither leads on the greater share of scored axes, so the decision turns on which constraint is binding rather than on an overall winner.

Where do AIRS Medical and Lunit differ most?

The widest separation the AI Health Index records between AIRS Medical and Lunit is on Model Supply Chain Disclosure, where AIRS Medical grades B and Lunit grades D. That axis sits in the AI Capability group, so it should carry the most weight for a buyer whose binding constraint is how much of the work the model itself is trusted to do.

Where do AIRS Medical and Lunit grade the same?

The AI Health Index grades AIRS Medical and Lunit the same on several axes, including AI Centrality, Autonomy and Oversight Model and HIPAA and BAA Posture. Neither holds an advantage the index can evidence on those axes, so they should not carry weight in a selection between these two.

What have AIRS Medical and Lunit not disclosed?

At the last review, at least one of AIRS Medical and Lunit published thin or absent detail on Model Supply Chain Disclosure. The AI Health Index treats an absent disclosure as a gap in the public record rather than a failure of the product, so these are the axes to get in writing during diligence instead of inferring from the grade.

Keep Comparing

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.

Disclosure

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.