Modella AI vs Paige (2026)
Two computational pathology companies at different points on the regulatory map. Paige produced the first authorised AI based digital pathology product, obtained through the De Novo route, which created the category and remains the reference point for what a reviewed pathology algorithm looks like. Modella builds generative and agentic tooling, including a co pilot pairing pathology foundation models with conversational interaction, which reaches tasks nobody wrote a specific algorithm for. These are not substitutes and should not be compared as such. A laboratory signing out cancer diagnoses needs the authorised detector. A research group or an academic department exploring what foundation models can do with a slide archive wants the co pilot, and should be clear with itself that using it to inform a diagnosis is a decision the laboratory is making, not one a regulator has reviewed.
- It builds generative and agentic tools for pathology, including a co pilot combining pathology foundation models with conversational interaction, which is a different layer from detection algorithms.
- Foundation models pretrained on pathology give it a route to tasks nobody wrote a specific algorithm for, which is where the field is heading.
- For a research or academic pathology department, a general purpose reasoning layer is more useful than a fixed set of cleared detectors.
- It holds the De Novo authorization that created the regulatory category for AI cancer detection support, which is the reference point everything else here is measured against.
- Authorised depth in prostate cancer detection means the capability has been reviewed rather than demonstrated.
- For a clinical laboratory signing out cases, an authorised product is a materially different proposition from a research grade co pilot.
This comparison is published by AI Health Index, an independent research platform that compares healthcare AI vendors objectively. Modella AI and Paige 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 | Modella AI | Paige |
|---|---|---|
| Primary category | Digital Pathology AI | Digital Pathology AI |
| Founded | 2023 | 2018 |
| Headquarters | Boston, Massachusetts, United States | New York, New York, United States |
| Website | modella.ai | paige.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 Modella AI its top capability grade on AI Centrality and Model and Technology Transparency. Set against Paige, Modella AI grades higher on Model and Technology Transparency and Setting and Specialty Coverage. 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
The AI Health Index awards Paige its top capability grade on AI Centrality and FDA and Regulatory Status. Set against Modella AI, Paige 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 Modella AI or Paige?
On the axes where the AI Health Index separates them, Modella AI grades higher on Model and Technology Transparency and Setting and Specialty Coverage, and Paige grades higher on several axes, including Model Supply Chain Disclosure, Clinical and Operational Evidence and Security Certifications and Trust Center. Paige 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 Modella AI and Paige differ most?
The widest separation the AI Health Index records between Modella AI and Paige is on FDA and Regulatory Status, where Modella AI grades C and Paige grades A. 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 Modella AI and Paige grade the same?
The AI Health Index grades Modella AI and Paige the same on several axes, including AI Centrality, Autonomy and Oversight Model 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 Modella AI and Paige not disclosed?
At the last review, at least one of Modella AI and Paige 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 Digital Pathology AI page.
The regulatory positions are not comparable and should not be presented as competing claims: one holds an authorisation for a specific detection task and the other builds general purpose pathology tooling that is not a cleared diagnostic. A foundation model co pilot answering questions about a slide sits outside the framework that governs a detection algorithm, and a laboratory using it to inform a diagnosis is making that decision itself. Neither publishes pricing. Neither publishes a subgroup or site level performance analysis, and staining and scanner variation between laboratories remains the largest uncontrolled variable in both.