Diagnostics & imaging AI · Radiology AI marketplace and platform
CARPL.ai
CARPL.ai is a radiology AI platform whose single PACS connection lets hospitals find, test, deploy and monitor more than 300 imaging AI apps from over 100 developers.
Strong fit
- Radiology groups that want many imaging AI tools through one connection
- Health systems that want to test AI models on their own scans before buying
- Governments and large networks that need a vendor-neutral way to run imaging AI
Weak fit
- Practices that want one detection algorithm and nothing else
- Buyers who want AI outside imaging today
- Buyers who need a posted price
Bottom line
CARPL.ai is a good choice for radiology groups and health systems that want to discover, test and run imaging AI from many vendors through one platform. Its marketplace is large and its validation tools are a real help when choosing models. Buyers still pay for each AI app, so the total price needs careful checking.
Score breakdown
Weights: Outcomes 35% · Product 30% · Implementation 20% · Pricing clarity 15%.
Dr. Vidur Mahajan founded CARPL.ai and launched it in 2021, after years of testing AI at Mahajan Imaging, his family's radiology business in India. He is chief executive. The company has teams in California and India and raised a $10 million Series A led by the International Finance Corporation in July 2026.
CARPL connects once to a hospital's PACS and gives it access to more than 300 imaging AI apps from over 100 developers. Teams can find an app, try it on their own studies, measure how well it performs against their radiologists and then switch it on in the reading workflow.
After go-live, CARPL routes studies to the right apps, sends results back to the radiologist and tracks how each model performs over time. Customers can also build and test their own models on the platform, which stays neutral between AI vendors and between cloud and on-site hosting.
CARPL says four of the five largest private radiology groups use it, along with Massachusetts General Hospital, Radiology Partners, University Hospitals and I-MED, and governments in Brazil, India, Singapore, Spain and the UAE. It plans to move into pathology, genomics and general medicine.
CARPL does not post prices, and each AI app has its own license on top of the platform. Buyers should add up the full cost of the apps they expect to run.
deepc fits health systems that want one integration layer to run and govern regulatory-approved AI, with a strong base in Europe. 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 |
| deepc | 6.8 | 7.0 |
| Ferrum Health | 6.8 | 6.5 |
Pricing
| Item | Detail |
|---|---|
| Model | CARPL.ai sells platform access by subscription, and customers license the AI apps they deploy through the marketplace. |
| What usually drives cost | For CARPL.ai, cost depends on study volume, the number of sites and the AI apps you run, each of which carries its own license. |
| What to ask before you buy | Ask CARPL.ai for the platform price and the cost of each app you plan to deploy, which PACS it has integrated at sites like yours, and how it monitors model performance after go-live. |
| Published pricing | CARPL.ai 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.