Paige vs PathAI (2026)
The comparison the whole pathology lane is measured against, and it did not exist in this index until now. Paige holds the historically decisive credential, the first authorization for an AI based digital pathology product, obtained through the De Novo route, which created the regulatory category everything since has used. PathAI holds the broader current position: clearance for its image management system for primary diagnosis, a large portfolio of interpretation modules, qualification of a tool under a drug development regime that subjected the model to review almost nothing else here has faced, and a model change plan a regulator has already looked at. For a laboratory buying today, PathAI covers more of the workflow and publishes more about how the models are governed. Paige remains the reference point for what an authorised pathology algorithm looks like.
- It produced the first authorization for an AI based digital pathology product through the De Novo route, which created the regulatory category the rest of this lane now files into.
- It takes a route to accountability nothing else in this index takes, which is worth understanding directly rather than through a grade.
- Depth in prostate cancer detection is authorised rather than claimed, with foundation model scale work behind the broader ambition.
- The evidence spans regulatory, commercial and academic axes at once, including a drug development tool qualification that put a pathology model through a review process almost nothing else here has faced.
- Model change is governed by a plan a regulator has reviewed, which is a materially stronger mechanism than a published principles page.
- Both cloud and on premise deployment are offered, a certification is stated openly alongside the most substantial vulnerability disclosure programme in this lane, and the business associate position is the best stated here.
This comparison is published by AI Health Index, an independent research platform that compares healthcare AI vendors objectively. Paige and PathAI 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 | Paige | PathAI |
|---|---|---|
| Primary category | Digital Pathology AI | Digital Pathology AI |
| Founded | 2018 | 2016 |
| Headquarters | New York, New York, United States | Boston, Massachusetts, United States |
| Website | paige.ai | pathai.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.
The AI Health Index grades Paige A on AI Centrality and A on FDA and Regulatory Status, the second reflecting the first authorisation granted for an AI based digital pathology product, obtained through the De Novo route. Paige grades B on Autonomy and Oversight Model, B on Model and Technology Transparency, B on Clinical and Operational Evidence, B on Security Certifications and Trust Center, B on AI Governance and Bias Disclosure, B on EHR and Interoperability Depth, B on Deployment Model and Data Residency and B on AI Liability and Recourse, and C on AI Safety and PHI Stewardship, C on HIPAA and BAA Posture, C on Model Supply Chain Disclosure, C on Commercial Transparency and C on Setting and Specialty Coverage. Against PathAI the two share the top grade on AI Centrality and on FDA and Regulatory Status and separate almost everywhere else, with PathAI a grade ahead on Clinical and Operational Evidence, Model and Technology Transparency, AI Safety and PHI Stewardship, HIPAA and BAA Posture and Setting and Specialty Coverage, and the two level on Commercial Transparency.
Source: AI Health Index, August 2026
The AI Health Index grades PathAI A on AI Centrality, A on Clinical and Operational Evidence, A on Model and Technology Transparency and A on FDA and Regulatory Status, four top grades against Paige at two. PathAI grades B on Autonomy and Oversight Model, B on AI Safety and PHI Stewardship, B on HIPAA and BAA Posture, B on Security Certifications and Trust Center, B on AI Governance and Bias Disclosure, B on EHR and Interoperability Depth, B on Deployment Model and Data Residency, B on Setting and Specialty Coverage and B on AI Liability and Recourse, and C on Model Supply Chain Disclosure and C on Commercial Transparency. The AI Health Index reading is that PathAI carries no C grade anywhere except on what sits underneath its models and what they cost, while Paige carries five, so the published record on PathAI is the more complete of the two even though Paige holds the older regulatory credential.
Source: AI Health Index, August 2026
Questions buyers ask
Paige vs PathAI: which should a laboratory choose?
The AI Health Index grades both at the top on AI Centrality and on FDA and Regulatory Status, so the choice is not settled by regulatory standing and has to be made on what each publishes underneath it. PathAI holds the top grade on Clinical and Operational Evidence and on Model and Technology Transparency, which Paige grades B, and is a grade ahead of Paige on AI Safety and PHI Stewardship, HIPAA and BAA Posture and Setting and Specialty Coverage.
Paige holds the historically decisive credential, the first authorisation granted for an AI based digital pathology product, obtained through the De Novo route, which created the category later pathology products file into. A laboratory buying today has more published material to read on PathAI. A laboratory asking what an authorised pathology algorithm looks like is asking about Paige.
Which of Paige and PathAI publishes more clinical evidence?
PathAI, on the AI Health Index grading, which places it at A on Clinical and Operational Evidence against Paige at B. The AI Health Index grades that axis on what an outsider can retrieve and check rather than on the strength of the underlying science, so the gap records a difference in publication practice rather than a verdict on either company's research.
The caveat applying to both is that neither publishes subgroup or site level performance, and in digital pathology that omission is consequential, because stain protocol, scanner make, section thickness and the laboratory that cut the slide all move the input distribution before any patient variable does.
Are Paige and PathAI both FDA cleared?
Both hold the top grade on FDA and Regulatory Status in the AI Health Index, and they hold different objects. Paige holds a De Novo authorisation for an AI based digital pathology product for prostate cancer detection, the first granted in the category.
PathAI holds clearance for its image management system for primary diagnosis, along with qualification of an interpretation tool under a drug development regime and a predetermined change control plan, meaning model updates follow a path a regulator has already reviewed rather than arriving unannounced.
The AI Health Index keeps regulatory standing and clinical evidence on separate axes because a cleared product is not the same as a product with published evidence, and neither is the same as a product with published recourse when it is wrong.
Do Paige or PathAI publish pricing?
Neither does. The AI Health Index grades both Paige and PathAI C on Commercial Transparency, so a digital pathology buyer cannot establish a cost for either from public material and has to reach a sales conversation to do it. That is the norm rather than the exception in this category and across the AI Health Index generally, which is why commercial disclosure is graded as its own axis rather than folded into a score.
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.
These are the two names most pathology departments shortlist and their regulatory positions are not equivalent, so read them precisely rather than as competing claims of being first. One holds a De Novo authorization that created the category for AI cancer detection support; the other holds clearance for an image management system for primary diagnosis plus qualification of an interpretation tool under a separate regime. Those are different objects doing different jobs.
Neither publishes pricing of any kind, and neither publishes a bias evaluation or subgroup performance analysis, which in pathology matters because staining, scanner and preparation variation across laboratories affects model output before any patient variable does.