Diagnostics & imaging AI · X-ray detection AI
AZmed
AZmed builds the Rayvolve X-ray AI suite for fracture and chest detection as a second reader in radiology workflows. It is detection AI for X-ray, not a PACS replacement.
Strong fit
- Hospitals and imaging centers that want X-ray fracture and chest AI as a second reader
- Radiology leaders comparing mid-market detection suites rather than only enterprise triage platforms
- Buyers that need CE/FDA-scoped X-ray AI with RIS/PACS workflow hooks
Weak fit
- Health systems standardized on a single enterprise imaging AI platform with no room for another viewer
- Clinics without radiology ownership to act on AI flags
- Buyers that need ambient scribbling rather than imaging AI
Bottom line
AZmed earns a Conditional on our diagnostics and imaging AI board. The Paris company, led by co-founder and CEO Julien Vidal, sells the Rayvolve suite for fracture, chest, and related X-ray detection aimed at radiologists and emergency pathways. Third-party revenue estimates vary (~$6-12M band commonly cited), inside mid-market and under the hard cap. Implementation is a PACS/RIS integration and clinical validation project. Pricing is enterprise imaging AI SaaS. Score sits under Qure.ai and Riverain on commercial scale in the U.S. board and above thinner point tools when X-ray detection is the buy.
Score breakdown
Weights: Outcomes 35% · Product 30% · Implementation 20% · Pricing clarity 15%.
AZmed is a Paris radiology AI company co-founded by CEO Julien Vidal. Imaging teams use the Rayvolve suite to flag fractures and other X-ray findings as a second reader inside radiology workflows.
It is detection AI for X-ray, not an ambient scribe and not a full PACS. Compare Qure.ai when chest and emergency radiology AI is the peer frame, Riverain when chest imaging AI is the U.S. incumbent comparison, and DeepTek when a broader radiology AI platform is the shortlist.
Score reflects solid X-ray AI product depth with Conditional marks on U.S. scale and price clarity.
Competitor landscape
| Vendor | Overall | Ease of implementation |
|---|---|---|
| Aidoc | 7.9 | 7.5 |
| Qure.ai | 7.4 | 7.1 |
| Riverain Technologies | 7.3 | 6.9 |
| Studycast | 7.2 | 6.9 |
| Subtle Medical | 7.2 | 6.8 |
| DeepTek | 7.1 | 6.8 |
| AISAP | 7.0 | 6.6 |
| AZmed | 6.9 | 6.7 |
| Enlitic | 6.9 | 6.5 |
| Avicenna.AI | 6.9 | 6.6 |
| Ferrum Health | 6.8 | 6.5 |
| Koios Medical | 6.7 | 6.3 |
Pricing
| Item | Detail |
|---|---|
| Model | Enterprise imaging AI SaaS / per-study packaging; quote-based. |
| What usually drives cost | Study volume, modules (fracture, chest, measurements), and sites. |
| What to ask in diligence | Annual or per-study pricing at your X-ray volume, including PACS integration. |
| Published pricing | No public U.S. list price. |
Prerequisites for purchase
| Need | Why it matters |
|---|---|
| What you need to get AZmed to function | |
| Radiology champion named | Detection AI fails without readers who trust it. |
| PACS/RIS integration scoped | Viewer orphans do not change care. |
| Baseline miss and turnaround metrics | You cannot prove lift blind. |
| FDA/CE scope matched to your exams | Wrong clearance wastes go-live. |
| Alert fatigue rules defined | Too many flags get ignored. |
| What will maximize your value | |
| Pilot one exam type first | All X-rays day one multiplies noise. |
| Measure reader agreement at 90 days | Prove detection value. |
| Retire overlapping detection viewers | Duplicate alerts confuse radiologists. |
| Deal-breakers | |
| Ambient scribe only | |
| No PACS access | |
| No radiology owner | |
Value creation time frame
| # | Stage | Typical range |
|---|---|---|
| 1 | Security and PACS mapping | 4-8 weeks |
| 2 | Exam-type pilot | 6-12 weeks |
| 3 | Scale sites | 4-8 weeks |
Methodology
| Weight | Factor | What it measures |
|---|---|---|
| 35% | Customer outcomes | Whether buyers get measurable operational or clinical-workflow results after go-live |
| 30% | Product | Capability depth, reliability, and fit for the job the category actually buys |
| 20% | Implementation | How hard it is to stand up, integrate, train, and stabilize |
| 15% | Pricing clarity | Whether a buyer can model total cost without a mystery quote |
| Label | Meaning |
|---|---|
| Highly recommend | Strong outcomes and product with manageable caveats |
| Recommend | Solid fit for the right buyer; know the tradeoffs |
| Conditional | Only with a specific use case or heavy caveats |
| Not recommended | Avoid for most buyers in this category |
Read our full methodology for how we weight scores and assign recommend labels.