Nanox.AI vs TeraRecon (2026)
One of these finds things nobody asked about and the other is the road they travel on. Nanox.AI, formerly Zebra Medical Vision, mines CT scans taken for unrelated reasons to surface findings such as vertebral compression fractures and coronary calcium, with a deep clearance portfolio built over years and a listed parent whose filings describe the business. TeraRecon is the platform: vendor independent visualisation installed at roughly nineteen hundred sites with a marketplace of partner algorithms, so a health system integrates once rather than per algorithm. The two are complementary in practice. The question opportunistic screening raises, and neither vendor answers, is who pays for the consequences: a finding surfaced from a scan taken for something else creates a follow up obligation, and that cost lands on the health system, not on the algorithm.
- Its strategy is opportunistic population health rather than diagnostic breadth, mining CT scans that were taken for another reason to find findings nobody was looking for.
- A deep clearance portfolio built over years across several anatomical findings, which is the harder path than a single authorisation.
- As the subsidiary of a listed parent, its business is described in filed disclosures, which is a level of accountability private competitors do not carry.
- The installed visualisation base across roughly nineteen hundred sites is the distribution asset, so partner algorithms reach clinicians through infrastructure that already exists.
- Vendor independence from scanners and archives is a genuine architectural position rather than a marketing claim, and it is the reason a mixed estate can standardise on it.
- It holds an independent certification in its own name and has third party recognition behind the platform.
This comparison is published by AI Health Index, an independent research platform that compares healthcare AI vendors objectively. Nanox.AI 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 | Nanox.AI | TeraRecon |
|---|---|---|
| Primary category | Radiology & Imaging AI | Radiology & Imaging AI |
| Founded | 2014 | Not recorded |
| Headquarters | Neve Ilan, Israel | Cambridge, Massachusetts |
| Website | nanox.vision | 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 Nanox.AI its top capability grade on FDA and Regulatory Status. Set against TeraRecon, Nanox.AI grades higher on AI Centrality and FDA and Regulatory Status. Its thinnest published disclosure sits on Model Supply Chain Disclosure and AI Liability and Recourse. 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 Nanox.AI, TeraRecon grades higher on several axes, including Model Supply Chain Disclosure, Clinical and Operational Evidence 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
Questions buyers ask
Should we choose Nanox.AI or TeraRecon?
On the axes where the AI Health Index separates them, Nanox.AI grades higher on AI Centrality and FDA and Regulatory Status, and TeraRecon grades higher on several axes, including Model Supply Chain Disclosure, Clinical and Operational Evidence and Security Certifications and Trust Center. TeraRecon 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 Nanox.AI and TeraRecon differ most?
The widest separation the AI Health Index records between Nanox.AI and TeraRecon is on EHR and Interoperability Depth, where Nanox.AI grades C and TeraRecon grades A. That axis sits in the Integration and Deployment group, so it should carry the most weight for a buyer whose binding constraint is how the product lands in the stack already in place.
Where do Nanox.AI and TeraRecon grade the same?
The AI Health Index grades Nanox.AI and TeraRecon the same on several axes, including Autonomy and Oversight Model, Model and Technology Transparency and AI Safety and PHI Stewardship. 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 Nanox.AI and TeraRecon not disclosed?
At the last review, at least one of Nanox.AI and TeraRecon published thin or absent detail on Model Supply Chain Disclosure and AI Liability and Recourse. 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 Radiology & Imaging AI page.
Opportunistic screening carries a consequence that neither vendor addresses and buyers rarely price: findings surfaced from a scan taken for another reason generate a duty to act, follow up imaging, patient notification and an incidental finding pathway, and the cost of that pathway falls on the health system rather than the vendor.
Nanox publishes no health privacy commitment, no data governance framework and no subgroup analysis, which is a notable gap for a product designed to run across whole populations. TeraRecon routes model transparency to its partners with no published requirement on them. Neither publishes pricing.