QuantHealth vs Unlearn.AI (2026)
If your failure mode is buying breadth, 23 therapeutic areas and a headline 90 percent accuracy, without asking what validates it, this pair is a clean lesson in evidence posture. Both simulate trials before they run. QuantHealth's Large Real-World Drug Model is a general purpose foundation model trained on more than 350 million patients, spanning oncology, immunology, and cardiometabolic disease, with strong reported figures that are almost entirely vendor generated and carry no peer reviewed methodology or regulatory qualification. Unlearn.AI is the opposite trade: narrow, only where a disease specific digital twin generator exists, but its PROCOVA method holds a positive EMA qualification opinion, is published and patented with a public mathematical proof, and FDA CDER has stated it concurs. Pick breadth you must take on trust, or a bounded method a regulator has already vetted.
- Breadth is the commercial advantage: a general purpose model across a reported 23 therapeutic areas including oncology, immunology, and cardiometabolic disease, covering trial design, indication selection, drug repurposing, and probability of success, where Unlearn only operates where a disease specific generator exists.
- It reports scale and headline accuracy specifically, 88 percent for Phase 2 and 83.2 percent for Phase 3 predictions on a model trained across more than 350 million patients, and claims predictions without sponsor data.
- If your program spans indications with binary or time to event endpoints common in oncology and cardiovascular trials, QuantHealth's general model applies where Unlearn's EMA qualification, limited to continuous endpoints, does not.
- The regulatory standing is unique in this index: PROCOVA holds a positive EMA qualification opinion as an acceptable primary analysis approach for Phase 2 and 3 trials with continuous endpoints, and FDA CDER has stated it concurs, a regulator endorsing a method rather than clearing a device.
- The method is auditable from primary sources: published and patented, with a public mathematical proof and named authors, so a buyer or a regulator can verify it rather than trust a datasheet, which is why it could be qualified at all.
- Safety is solved by design, not policy: the team refused to use twins as external control arms and instead supplies only a prognostic covariate within a randomized trial, preserving randomization and bounding bias even when the model omits variables.
This comparison is published by AI Health Index, an independent research platform that compares healthcare AI vendors objectively. QuantHealth and Unlearn.AI 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 | QuantHealth | Unlearn.AI |
|---|---|---|
| Primary category | Clinical Trials AI | Clinical Trials AI |
| Founded | 2020 | 2017 |
| Headquarters | Tel Aviv, Israel | San Francisco, California, United States |
| Website | quanthealth.ai | unlearn.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 QuantHealth its top capability grade on AI Centrality. Set against Unlearn.AI, QuantHealth grades higher on 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 Unlearn.AI its top capability grade on several axes, including AI Centrality, Autonomy and Oversight Model and Model and Technology Transparency. Set against QuantHealth, Unlearn.AI grades higher on several axes, including Autonomy and Oversight Model, Model and Technology Transparency and Clinical and Operational Evidence. 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 QuantHealth or Unlearn.AI?
On the axes where the AI Health Index separates them, QuantHealth grades higher on Setting and Specialty Coverage, and Unlearn.AI grades higher on several axes, including Autonomy and Oversight Model, Model and Technology Transparency and Clinical and Operational Evidence. Unlearn.AI 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 QuantHealth and Unlearn.AI differ most?
The widest separation the AI Health Index records between QuantHealth and Unlearn.AI is on Model and Technology Transparency, where QuantHealth grades C and Unlearn.AI 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 QuantHealth and Unlearn.AI grade the same?
The AI Health Index grades QuantHealth and Unlearn.AI the same on several axes, including AI Centrality, Model Supply Chain Disclosure 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.
Related comparisons
Other published head to head assessments involving these vendors or their closest peers. The full set for this category is on the Clinical Trials AI page.
This is the index's clearest evidence posture contrast. Unlearn's claims sit in peer reviewed literature and a regulatory qualification, though several key demonstrations are simulations or retrospective applications rather than prospective use as the primary analysis of a completed registrational trial, and its EMA qualification is narrow to continuous endpoints.
QuantHealth's figures, up to 90 percent accuracy and 31.4 million dollars in savings for an unnamed pharmaceutical partner, are vendor generated with no peer reviewed methodology located and no definition of how accuracy is measured or whether predictions were made before readout.
Both share a governance risk worth naming: a model trained on historical data that predicts a non responder subgroup could narrow who a trial enrolls, shaping who the eventual therapy is studied in and labeled for. Neither publishes pricing.