Indica Labs vs Proscia (2026)
Both are FDA cleared platforms for primary diagnosis, so the question is not who is authorized but what shape of platform you want. If maximum scanner freedom and the broadest cleared scope matter most, start with Indica Labs. If you want a platform whose AI is more woven in and that carries both US and EU clearance in a single edition, start with Proscia. Indica leads on interoperability, cleared across two scanner makers with DICOM portability and outside algorithm hosting, and its clearance covers surgical pathology generally. Proscia scores its AI higher because the platform actively recommends stain panels and scores PD-L1, HER2, and Ki-67 in the viewer, and it holds the strongest oversight posture here, arguing publicly for expert verification rather than replacement.
- Widest scanner and interoperability freedom: cleared across Hamamatsu and Leica scanners, DICOM portable, and able to host outside algorithms, the only A on interoperability among these platforms.
- Broadest cleared scope: primary diagnosis of surgical pathology slides generally rather than a single tumor type, versus Proscia's narrower laboratory and life sciences footprint.
- More documented compliance controls: states HIPAA compliance plainly and adds ISO 13485 and 21 CFR Part 11 electronic record controls, more than Proscia publishes on either front.
- AI is more native to the workspace: it recommends stain panels and ancillary tests from what it sees in tissue and scores PD-L1, HER2, and Ki-67 inline, earning a higher AI centrality grade than Indica.
- Clearance in both jurisdictions from one edition: FDA 510(k) and CE-IVDR for primary diagnosis, with a deployment that is not locked to a specific scanner.
- The strongest oversight posture in this group: built around expert verification, with the company arguing publicly that the opportunity is removing administrative burden rather than diagnostic expertise.
This comparison is published by AI Health Index, an independent research platform that compares healthcare AI vendors objectively. Indica Labs and Proscia 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 | Indica Labs | Proscia |
|---|---|---|
| Primary category | Digital Pathology AI | Digital Pathology AI |
| Founded | 2007 | 2014 |
| Headquarters | Albuquerque, New Mexico, United States | Philadelphia, Pennsylvania, United States |
| Website | indicalab.com | proscia.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 Indica Labs its top capability grade on several axes, including FDA and Regulatory Status, EHR and Interoperability Depth and Deployment Model and Data Residency. Set against Proscia, Indica Labs grades higher on AI Governance and Bias Disclosure, EHR and Interoperability Depth and Setting and Specialty Coverage. 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, July 2026
The AI Health Index awards Proscia its top capability grade on several axes, including Autonomy and Oversight Model, Model Supply Chain Disclosure and AI Safety and PHI Stewardship. Set against Indica Labs, Proscia grades higher on several axes, including AI Centrality, Autonomy and Oversight Model and Model and Technology Transparency. 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, July 2026
Questions buyers ask
Should we choose Indica Labs or Proscia?
On the axes where the AI Health Index separates them, Indica Labs grades higher on AI Governance and Bias Disclosure, EHR and Interoperability Depth and Setting and Specialty Coverage, and Proscia grades higher on several axes, including AI Centrality, Autonomy and Oversight Model and Model and Technology Transparency. Proscia 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 Indica Labs and Proscia differ most?
The widest separation the AI Health Index records between Indica Labs and Proscia is on Model Supply Chain Disclosure, where Indica Labs grades C and Proscia grades A. 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 Indica Labs and Proscia grade the same?
The AI Health Index grades Indica Labs and Proscia the same on several axes, including HIPAA and BAA Posture, FDA and Regulatory Status and AI Liability and Recourse. 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 Digital Pathology AI page.
Neither publishes pricing, and neither holds a SOC 2, HITRUST, or ISO 27001 attestation; Proscia's GxP readiness is a research quality framework, not information security. Indica's clearance is specific to named scanners, so hardware and software cost are a coupled decision, and it documents its own AI models thinly.
Proscia holds whole slide images plus clinical metadata for both diagnostic laboratories and pharmaceutical research customers and does not publish where the boundary between those two data environments sits, nor its hosting or data residency terms. Both host outside algorithms that carry their own separate regulatory classifications, so platform clearance does not extend to the algorithms running on it, and each must be evaluated on its own.