Diagnostics & imaging AI · Clinical AI platform for health systems
deepc
deepc is a Munich, Germany, company whose deepcOS platform connects once to hospital systems so health systems can deploy, monitor and govern regulatory-approved AI from many vendors, starting in radiology.
Strong fit
- European hospitals and NHS trusts that want one platform for regulatory-approved imaging AI
- Health systems that want governance and monitoring for AI from many vendors
- Groups that want to deploy their own in-house models through the same platform
Weak fit
- US buyers who want many local reference sites
- Buyers who want published outcome data before signing
- Buyers who need a posted price
Bottom line
deepc is a conditional choice for hospitals, mainly in Europe and the UK, that want one platform to deploy and govern clinical AI from many vendors. Its base in radiology AI is solid, but it is widening into a general AI platform and publishes few hard results. Ask for references at sites like yours.
Score breakdown
Weights: Outcomes 35% · Product 30% · Implementation 20% · Pricing clarity 15%.
Dr. Franz Pfister and his co-founders started deepc in Munich, Germany, in 2019. Pfister, a physician and data scientist, is chief executive. The company has raised $30 million, most recently a 2024 Series A extension co-led by Sofinnova Partners and Bertelsmann Investments.
deepcOS connects once to a hospital's PACS, EHR and reporting systems and gives clinicians access to regulatory-approved AI from many vendors, covering more than 60 clinical uses in radiology. deepc handles the integration, routes studies to the right model and shows results inside the radiologist's workflow.
deepc now pitches itself as infrastructure for all clinical AI, not only imaging. The same layer is meant to run in-house models, coding and documentation tools, with one approval process, monitoring for performance drift and a record of how each model is used.
Users include LMU University Hospital and the Vivantes group in Germany, several NHS trusts in England, university hospitals in Switzerland and Norway, and Meritus Health in Maryland. Its outcome figures on its own site are models and estimates more than measured results.
deepc does not post prices, and its US customer base is small. Buyers should ask for references and results from health systems like theirs.
CARPL.ai fits radiology groups that want the largest imaging AI marketplace with tools to test models on their own scans. Ferrum Health fits US health systems that want to manage and monitor many AI models with evidence from their own sites.
Competitor landscape
| Vendor | Overall | Ease of implementation |
|---|---|---|
| CARPL.ai | 7.3 | 7.6 |
| Ferrum Health | 6.8 | 6.5 |
| deepc | 6.8 | 7.0 |
Pricing
| Item | Detail |
|---|---|
| Model | deepc sells deepcOS by enterprise subscription, with third-party AI apps licensed through the platform. |
| What usually drives cost | For deepc, cost depends on the number of sites and connected systems, study volume and the AI apps you deploy. |
| What to ask before you buy | Ask deepc for a platform price and per-app costs, which PACS and EHR systems it has connected in your country, and results from health systems of your size. |
| Published pricing | deepc does not publish a price list. |
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.