Diagnostics & imaging AI · CT radiology AI
Avicenna.AI
Avicenna.AI builds FDA-cleared CT radiology AI (CINA portfolio) for emergency and incidental findings, with orchestration meant to deliver results inside existing PACS/RIS workflows. It is not a POCUS-only or ambient scribe product.
Strong fit
- Hospitals and teleradiology groups that need FDA-cleared CT triage AI for neurovascular, PE, spine, and related emergency findings
- Teams that can integrate results into existing PACS/RIS without adding another viewer staff must live in
- Buyers comparing focused CT AI suites rather than ambient documentation tools
Weak fit
- Sites without CT emergency volume to justify triage AI
- Buyers who need POCUS-only AI (AISAP lane) or ambient scribes
- Organizations that will not staff alert review and false-positive handling
Bottom line
Avicenna.AI earns a Conditional for imaging programs specifically buying CT triage and incidental-finding AI with FDA-cleared CINA modules. Public materials cite multiple FDA clearances, roughly 300 installed sites, and a 2025 AVI orchestration layer meant to keep radiologists in native PACS/RIS. Outcomes depend on whether alerts change disposition without drowning readers in noise. Product depth is competitive in CT emergency AI but narrower and earlier-scale than Aidoc on many U.S. enterprise shortlists. Implementation is a radiology IT project. Pricing is custom. LinkedIn-class revenue estimates near $3.5M put it small even inside our prefer band - Conditional reflects scale and coverage limits more than a bad clinical idea. Score sits just under AISAP and above Ferrum on our diagnostics board.
Score breakdown
Weights: Outcomes 35% · Product 30% · Implementation 20% · Pricing clarity 15%.
Avicenna.AI builds FDA-cleared CT radiology AI (CINA portfolio) for emergency and incidental findings, with an AVI orchestration layer aimed at PACS/RIS-native delivery.
Compare Aidoc for broader enterprise radiology AI, AISAP for POCUS, and Studycast when cloud imaging workflow is the job instead of triage alerts.
Conditional reflects earlier U.S. scale and pricing opacity versus category leaders on our diagnostics board.
Competitor landscape
| Vendor | Overall | Ease of implementation |
|---|---|---|
| Avicenna.AI | 6.9 | 6.6 |
| Aidoc | 7.9 | 7.5 |
| Rad AI | 7.7 | 7.2 |
| Subtle Medical | 7.2 | 6.8 |
| AISAP | 7.0 | 6.6 |
| Studycast | 7.2 | 6.9 |
Pricing
| Item | Detail |
|---|---|
| Model | Software licensing for CT AI modules and orchestration; site and volume based. |
| What usually drives cost | Modules cleared for your use cases, sites, study volume, and orchestration packaging. |
| What to ask in diligence | Cost per site for the CINA modules you will enable, AVI integration scope, and false-positive handling expectations. |
| Published pricing | Public list price: not published. Expect a radiology AI program quote. |
Match FDA-cleared indications to the CT exams you actually run before enterprise rollout.
Prerequisites for purchase
| Need | Why it matters |
|---|---|
| What you need to get Avicenna.AI to function | |
| Radiology or cardiology clinical owner plus imaging IT | AI or workflow tools fail without both. |
| PACS/RIS/EMR interface path documented | Orphan results queues create safety risk. |
| Agreement on which exam types are in scope | Boiling the ocean with every modality delays value. |
| Alert or report review staffing plan | Unowned queues become ignored queues. |
| Change control for clinical downtime windows | Surprise viewers during peak lists destroy trust. |
| What will maximize your value | |
| Measure turnaround and reopen/edit rates | Demo accuracy is not operations. |
| Start with one high-volume exam family | Narrow wins beat empty enterprise banners. |
| Review false positives weekly with radiologists | Noisy tools get turned off. |
| Keep radiologists in native viewers when possible | Extra worklists kill adoption. |
| Document FDA indication coverage vs your protocol mix | Mismatched clearances waste spend. |
| Deal-breakers | |
| No imaging IT or clinical owner. | |
| You refuse any interface work. | |
| You expect ambient scribe outcomes from imaging AI. | |
| Nobody will review alerts or structured reports. | |
| Legal blocks cloud or vendor PHI paths you require. | |
Value creation time frame
| # | Stage | Typical range |
|---|---|---|
| 1 | Scope | 2–4 weeks — Exam list, interfaces, success metrics. |
| 2 | Integrate | 4–12 weeks — PACS/RIS/EMR hooks; validation cases. |
| 3 | Pilot | 4–8 weeks — One site or modality family. |
| 4 | Expand | Ongoing — More sites; alert tuning. |
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.