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 AI to check X-rays for fractures and chest findings as a second reader
- Radiology leaders comparing focused detection tools, not only large triage platforms
- Buyers that need X-ray AI cleared in Europe (CE) or by the FDA that connects to RIS and PACS
Weak fit
- Health systems committed to one imaging AI platform, with no room for another viewer
- Clinics with no radiology lead to act on AI flags
- Buyers who need AI note-writing rather than imaging AI
Bottom line
AZmed is worth considering for radiology teams and emergency departments that want AI to flag fractures and other findings on X-rays. It suits buyers ready for a PACS integration and clinical validation. Buyers who want larger U.S. scale should compare Qure.ai and Riverain.
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 its Rayvolve suite as a second reader that flags fractures, chest findings, and other X-ray results in radiology and the emergency department.
It is detection AI for X-rays. It does not write clinical notes, and it is not a full PACS.
Putting it in place means connecting it to PACS and RIS systems and testing it clinically. Pricing follows the usual software model for hospital imaging AI.
Among the imaging AI tools we review, AZmed scores below Qure.ai and Riverain on U.S. commercial scale. It scores above narrower tools when X-ray detection is what you are buying.
Qure.ai is the closest comparison for chest and emergency radiology AI. Riverain is the established U.S. option for chest imaging AI, and DeepTek fits buyers looking for a broader radiology AI platform.
AZmed's Conditional rating reflects solid X-ray AI, held back by its U.S. scale and unclear pricing.
Competitor landscape
| Vendor | Overall | Ease of implementation |
|---|---|---|
| Aidoc | 7.9 | 7.5 |
| ScreenPoint Medical | 7.9 | 7.6 |
| Harrison.ai | 7.7 | 7.5 |
| Qure.ai | 7.4 | 7.1 |
| Riverain Technologies | 7.3 | 6.9 |
| Subtle Medical | 7.2 | 6.8 |
| DeepTek | 7.1 | 6.8 |
| 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 | Imaging AI software, sold as a subscription or priced per study, with quotes for each customer. |
| What usually drives cost | Scan volume, which modules you add (fracture, chest, measurements), and the number of sites. |
| What to ask before you buy | The yearly or per-study price at your X-ray volume, including the PACS connection. |
| Published pricing | AZmed does not publish a U.S. price list. |
Prerequisites for purchase
| Need | Why it matters |
|---|---|
| What you need to get AZmed to function | |
| A named radiologist to lead it | Detection AI fails if radiologists do not trust it. |
| A plan for connecting to PACS and RIS | Results in a separate viewer no one opens do not change care. |
| Current numbers for missed findings and turnaround | Without them you cannot show improvement. |
| FDA and CE clearances that match your exams | The wrong clearance wastes the launch. |
| Rules to keep alerts from overwhelming readers | Too many flags get ignored. |
| What will maximize your value | |
| Start with one exam type | Covering every X-ray on day one multiplies false alarms. |
| Measure how often radiologists agree with it at 90 days | This shows whether detection adds value. |
| Turn off other detection viewers that overlap | Duplicate alerts confuse radiologists. |
| Deal-breakers | |
| You only want an AI scribe | |
| You cannot give access to PACS | |
| No radiologist owns it | |
Value creation time frame
| # | Stage | Typical range |
|---|---|---|
| 1 | Security review and PACS connection planning | 4-8 weeks |
| 2 | Pilot with one exam type | 6-12 weeks |
| 3 | Add more sites | 4-8 weeks |
Leadership
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 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.