Annalise.ai vs Behold.ai (2026)
Two chest radiograph companies separated by whether a human still reads the film. Annalise reads comprehensively across a large findings set with regulatory footprints in several markets and greater deployment maturity, and the radiologist decides every case. Behold auto reports films it classifies as high confidence normal, taking them off the worklist entirely, and publishes the peer reviewed performance numbers that autonomy demands. For a department whose backlog is normal films, Behold is doing something Annalise does not attempt and the saving is structural rather than incremental. The condition is that its false negative rate at the operating threshold has to be published, revalidated locally and monitored, because on an auto reported study nobody is ever going to catch the miss.
- Comprehensive chest radiograph coverage across a large findings set, with regulatory footprints in multiple markets and a joint venture supplying real reading volume.
- The reader retains the decision on every study, which is the conventional and lower risk configuration.
- Deployment maturity is greater, which matters for a modality where the failure is a subtle finding missed at scale.
- It removes studies from the worklist entirely by auto reporting films classified as high confidence normal, which is genuine autonomy inside a bounded task.
- The performance numbers that autonomy requires are published in peer reviewed venues, and the confidence threshold is itself the governance mechanism.
- For a service drowning in normal chest films, removing them from the queue is a different economic proposition from reading them faster.
This comparison is published by AI Health Index, an independent research platform that compares healthcare AI vendors objectively. Annalise.ai and Behold.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
| Fact | Annalise.ai | Behold.ai |
|---|---|---|
| Primary category | Radiology & Imaging AI | Radiology & Imaging AI |
| Founded | 2019 | 2015 |
| Headquarters | Sydney, Australia | London, United Kingdom |
| Website | annalise.ai | behold.ai |
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 Annalise.ai its top capability grade on AI Centrality, FDA and Regulatory Status and Setting and Specialty Coverage. Set against Behold.ai, Annalise.ai grades higher on several axes, including Model Supply Chain Disclosure, FDA and Regulatory Status and Deployment Model and Data Residency. 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 Behold.ai its top capability grade on AI Centrality and Autonomy and Oversight Model. Set against Annalise.ai, Behold.ai grades higher on Autonomy and Oversight Model, AI Liability and Recourse and EHR and Interoperability Depth. 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
Questions buyers ask
Should we choose Annalise.ai or Behold.ai?
On the axes where the AI Health Index separates them, Annalise.ai grades higher on several axes, including Model Supply Chain Disclosure, FDA and Regulatory Status and Deployment Model and Data Residency, and Behold.ai grades higher on Autonomy and Oversight Model, AI Liability and Recourse and EHR and Interoperability Depth. Annalise.ai 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 Annalise.ai and Behold.ai differ most?
The widest separation the AI Health Index records between Annalise.ai and Behold.ai is on FDA and Regulatory Status, where Annalise.ai grades A and Behold.ai grades C. That axis sits in the Regulatory and Compliance group, so it should carry the most weight for a buyer whose binding constraint is where regulatory exposure sits and who carries it.
Where do Annalise.ai and Behold.ai grade the same?
The AI Health Index grades Annalise.ai and Behold.ai the same on several axes, including AI Centrality, Model and Technology Transparency and Clinical and Operational Evidence. 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 Annalise.ai and Behold.ai not disclosed?
At the last review, at least one of Annalise.ai and Behold.ai 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.
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.
The autonomy difference sets the evidence bar: an auto reporting normal classifier needs its false negative rate at the operating threshold published and locally revalidated, because a missed finding on an auto reported film is never seen by anyone. Behold's regulatory standing is multi jurisdictional and unsettled and its corporate history complicates the identity of the contracting entity, both of which should be established before deployment. Neither publishes a security attestation or pricing, and findings counts are not comparable between them.