Aidoc vs Gleamer (2026)

AI Health Index verifiedAugust 4, 2026
Verdict

A platform against a specialist, and both are strong records. Aidoc runs detection models across an imaging estate through an operating system that handles normalisation, continuous monitoring and governance, with a 2026 clearance covering many acute indications from one named foundation model. Gleamer does trauma radiography, with a fracture detection clearance covering adult and paediatric use that is reported to be unique in that segment and one of the deepest evidence bases in musculoskeletal imaging. If your department needs one specific reading improved, particularly the overnight trauma queue, Gleamer is the sharper instrument and reaches you through several platforms. If you expect to run clinical AI across modalities for a decade, Aidoc is selling the layer that survives whichever algorithms you replace.

The case for Aidoc
  • It runs many detection models across an imaging estate through an operating system handling normalisation, monitoring and governance for every model on it.
  • The 2026 clearance covers a double digit set of acute indications from a single named foundation model, with performance from a regulator reviewed pivotal study.
  • For a health system standardising clinical AI across modalities, the infrastructure is the durable purchase rather than any one algorithm.
The case for Gleamer
  • Its fracture detection clearance covers both adult and paediatric use, reported to be unique in that segment, with a clearance study design stronger than most here.
  • The evidence base is one of the deepest in musculoskeletal imaging, and trauma radiography is the highest volume plain film reading in any emergency department.
  • Distribution breadth substitutes for integration engineering, available directly and through several imaging platform partners.

This comparison is published by AI Health Index, an independent research platform that compares healthcare AI vendors objectively. Aidoc and Gleamer 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 Aidoc Gleamer
Primary category Radiology & Imaging AI Radiology & Imaging AI
Founded Not recorded 2017
Headquarters Tel Aviv, Israel Paris, France
Website aidoc.com gleamer.ai
Attribute Matrix

Side by Side

Axis
A
Aidoc
G
Gleamer
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.

Aidoc

The AI Health Index awards Aidoc its top capability grade on several axes, including AI Centrality, Autonomy and Oversight Model and Model and Technology Transparency. Set against Gleamer, Aidoc grades higher on several axes, including Autonomy and Oversight Model, Model and Technology Transparency and 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

Gleamer

The AI Health Index awards Gleamer its top capability grade on AI Centrality, Clinical and Operational Evidence and FDA and Regulatory Status. Set against Aidoc, Gleamer does not grade higher on any scored axis, though the two are level on several axes, including AI Centrality, Clinical and Operational Evidence and FDA and Regulatory Status. 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 Aidoc or Gleamer?

The AI Health Index grades Aidoc higher than Gleamer on every axis that separates them, several axes, including Autonomy and Oversight Model, Model and Technology Transparency and Model Supply Chain Disclosure. Gleamer does not grade higher on any scored axis.

Where do Aidoc and Gleamer differ most?

The widest separation the AI Health Index records between Aidoc and Gleamer is on Model Supply Chain Disclosure, where Aidoc grades A and Gleamer grades C. 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 Aidoc and Gleamer grade the same?

The AI Health Index grades Aidoc and Gleamer the same on several axes, including AI Centrality, Clinical and Operational Evidence and FDA and Regulatory Status. Neither holds an advantage the index can evidence on those axes, so they should not carry weight in a selection between these two.

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 Clinical Decision Support page.

Disclosure

The comparison is between a platform and a point solution, so the honest question is whether you are buying infrastructure or a specific reading. Gleamer's multi party distribution model makes data stewardship harder to answer, since no business associate terms were located and the responsible party varies by delivery path, and it publishes no security attestation, which this index records as a pattern across non United States imaging vendors rather than a fault unique to it. Neither publishes a subgroup performance analysis, and prevalence in your own population determines the false positive burden more than published sensitivity does.