HOPPR vs TeraRecon (2026)
Two companies whose customers are other companies, sitting one layer apart in the same supply chain. HOPPR supplies the foundation model and development environment that imaging AI is built on, with model transparency among the strongest in this index and a stated consent basis for its training data. TeraRecon distributes the finished applications, with advanced visualisation installed at roughly nineteen hundred sites and a marketplace so a health system integrates once rather than per algorithm. Neither is bought by a radiologist, and together they explain a structural problem: a hospital running a third party algorithm through a marketplace has two layers of provenance above it that nobody in the purchasing chain inspects, and neither layer publishes what its models were trained on.
- It is the layer beneath the applications, a multimodal foundation model and development environment sold to developers, AI companies and imaging vendors.
- Model and technology disclosure is among the strongest in this index, appropriate when others build regulated products on it.
- The consent basis for training data is stated rather than implied, which almost nothing in this lane does.
- The installed advanced visualisation base across roughly nineteen hundred clinical sites is the distribution asset, independent of any scanner or archive vendor.
- The marketplace lets a health system run many partner algorithms through one platform rather than integrating each separately.
- It holds an independent certification in its own name with third party recognition behind the platform.
This comparison is published by AI Health Index, an independent research platform that compares healthcare AI vendors objectively. HOPPR and TeraRecon 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 | HOPPR | TeraRecon |
|---|---|---|
| Primary category | Radiology & Imaging AI | Radiology & Imaging AI |
| Founded | 2019 | Not recorded |
| Headquarters | Chicago, Illinois | Cambridge, Massachusetts |
| Website | hoppr.ai | terarecon.com |
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 HOPPR its top capability grade on AI Centrality, Model and Technology Transparency and Model Supply Chain Disclosure. Set against TeraRecon, HOPPR grades higher on several axes, including AI Centrality, 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
The AI Health Index awards TeraRecon its top capability grade on EHR and Interoperability Depth and Setting and Specialty Coverage. Set against HOPPR, TeraRecon grades higher on several axes, including Autonomy and Oversight Model, Clinical and Operational Evidence and 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 HOPPR or TeraRecon?
On the axes where the AI Health Index separates them, HOPPR grades higher on several axes, including AI Centrality, Model and Technology Transparency and Model Supply Chain Disclosure, and TeraRecon grades higher on several axes, including Autonomy and Oversight Model, Clinical and Operational Evidence and EHR and Interoperability Depth. HOPPR 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 HOPPR and TeraRecon differ most?
The widest separation the AI Health Index records between HOPPR and TeraRecon is on AI Centrality, where HOPPR grades A and TeraRecon 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 HOPPR and TeraRecon grade the same?
The AI Health Index grades HOPPR and TeraRecon the same on several axes, including Security Certifications and Trust Center, FDA and Regulatory Status and AI Governance and Bias Disclosure. 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 occupy adjacent positions in the same supply chain, one supplying the models and the other distributing finished applications, and neither is bought by a clinician. Both create the same structural blindness: a platform imposes no published performance requirement on the algorithms it hosts, and a foundation model publishes no subgroup analysis or training data composition, so a hospital running an algorithm on a marketplace has two layers of provenance it cannot inspect. Neither publishes pricing.