Every claim we hold, and how far we can stand behind it.
142 claims across 39 products. 28% are confirmed against the source text by the evidence gate; 39% come from a class with no vendor interest. Everything below is browsable, filterable, and available as JSON.
| Evidence class | Claims | Confirmed | What it can support |
|---|---|---|---|
| Peer-reviewed study | 19 | 5 | Reviewed before publication; assess study quality, funding, conflicts, and relevance to your setting. |
| Regulatory filing | 4 | 0 | A statement made to a regulator, where being wrong has consequences. |
| Independent report | 32 | 9 | Reporting outside a vendor publication; assess underlying data, sponsorship, and conflicts separately. |
| Customer case | 4 | 3 | A real deployment, but selected and framed by someone with an interest. |
| Press release | 63 | 22 | The vendor's own announcement. Useful for dates and facts, not for outcomes. |
| Vendor page | 20 | 1 | Marketing copy. Establishes what is claimed, never that it is true. |
Showing 2 of 2 matching claims (142 in the full corpus) · 0% of this selection is confirmed · clear filters
Independent BMJ Open peer-reviewed vignette study (Gilbert et al., 2020) compared digital symptom assessment apps, including Ada, against GPs for suggesting conditions and urgency advice
Provenance
- Claim ID
- clm_796e5c36fb879e0a483b
- Version
- 1
- Workflow
- Clinical Evidence & Decision Support
- Dimension
- evidence strength
- Retrieved
- 2026-08-27
- Reviewer
- healthit-gate-v1
- Source URL
- https://pmc.ncbi.nlm.nih.gov/articles/PMC7745718/
Lunit INSIGHT MMG (Lancet Digital Health 2020): the AI algorithm was developed and validated on 170,230 mammography examinations from five institutions; in a multicentre, observer-blinded reader study of 320 mammograms read by 14 radiologists, AI performance (AUROC 0.940) significantly exceeded radiologists without assistance (AUROC 0.810, p<0.0001), and AI assistance improved radiologists' performance to AUROC 0.881 (p<0.0001).
Provenance
- Claim ID
- clm_9953a345495ad65fe703
- Version
- 1
- Workflow
- Imaging & Diagnostic AI
- Dimension
- evidence strength
- Retrieved
- 2026-08-27
- Reviewer
- healthit-gate-v1
- Source URL
- https://doi.org/10.1016/S2589-7500(20)30003-0