Aidoc vs Rad AI (2026)
The two most commonly shortlisted radiology AI purchases, and they do not overlap at all. Aidoc works on the image, running detection models across the imaging estate through an operating system that handles normalisation, monitoring and governance, with a 2026 clearance covering many acute indications from one named foundation model. Rad AI never touches the image, generating the report impression from the radiologist's own dictated findings and learning their individual language from prior reports, with the clearest structural oversight model in this index because the workflow enforces it. Detection changes which study gets read next; reporting changes how long each one takes to finish. If your backlog is turnaround time on urgent findings, Aidoc. If it is that radiologists are reading fast and reporting slowly, Rad AI is aimed at the actual bottleneck.
- It works on the image, running detection models across an imaging estate through an operating system that handles normalisation, monitoring and governance for every model it runs.
- The January 2026 clearance covers many acute indications from a single named foundation model, with performance from a regulator reviewed pivotal study rather than a vendor benchmark.
- For a department whose problem is that urgent findings wait in the queue, detection and worklist reprioritisation is the intervention.
- It works on the report, generating the impression from the radiologist's own dictated findings and learning that radiologist's language from their prior reports.
- The oversight model is structurally the clearest in this index because the workflow enforces it: the model drafts and the radiologist signs, so no unread text reaches a released report.
- It publishes specific falsifiable operational figures with named baselines, including follow up completion, rather than adoption counts.
This comparison is published by AI Health Index, an independent research platform that compares healthcare AI vendors objectively. Aidoc and Rad AI 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
Plain facts
Side by Side
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.
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 Rad AI, Aidoc grades higher on several axes, including Model and Technology Transparency, Model Supply Chain Disclosure and Clinical and Operational Evidence. 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
The AI Health Index awards Rad AI its top capability grade on AI Centrality, Autonomy and Oversight Model and EHR and Interoperability Depth. Set against Aidoc, Rad AI grades higher on EHR and Interoperability Depth. 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
Questions buyers ask
Should we choose Aidoc or Rad AI?
On the axes where the AI Health Index separates them, Aidoc grades higher on several axes, including Model and Technology Transparency, Model Supply Chain Disclosure and Clinical and Operational Evidence, and Rad AI grades higher on EHR and Interoperability Depth. Aidoc leads on the greater share of scored axes, but the split means the decision turns on which constraint is binding rather than on an overall winner.
Where do Aidoc and Rad AI differ most?
The widest separation the AI Health Index records between Aidoc and Rad AI is on Model Supply Chain Disclosure, where Aidoc grades A and Rad AI 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 Rad AI grade the same?
The AI Health Index grades Aidoc and Rad AI 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.
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 are complementary and a large department will plausibly run both, so the useful question is sequencing rather than selection: detection changes which study is read next, reporting changes how long each study takes to finish. Rad AI holds no clearance for any product in its portfolio, which is consistent with reporting assistance rather than diagnosis but should be checked against how your department intends to use carried forward prior findings. Neither publishes pricing. Neither publishes subgroup performance, and for detection models trained on particular scanner and protocol mixes, local validation before go live is the diligence that gets skipped most often.