
Medical AI company
Avicenna.AI
French developer of CINA AI tools that flag critical and incidental findings on CT, covering neurovascular, chest and spine.
Company overview
Avicenna.AI develops AI software that analyses CT exams as soon as they are acquired and flags critical and incidental findings in the PACS. Its CINA range covers intracranial hemorrhage, large vessel occlusion, ASPECTS, pulmonary embolism, aortic dissection and spine fractures. It was incorporated in La Ciotat, France, in 2018.
- Headquarters
- La Ciotat, France
- Founded
- 2018 source
- Company type
- AI Technology Developer
- Deployment
- PACS-integrated
In its own words: “Founded in 2018, Avicenna.AI is a medical AI company formed through a collaboration between Cyril Di Grandi, a software and medical device expert, and Peter Chang, MD, a radiologist and AI expert.” source
AI products and platforms
- CINA-ICHAI-powered
Intracranial hemorrhage detection on CT
Product page ↗ - CINA-LVOAI-powered
Large vessel occlusion detection
Product page ↗ - CINA-PEAI-powered
Pulmonary embolism detection
Product page ↗ - CINA-CSpineAI-powered
Cervical spine fracture detection
Product page ↗
Technology categories
Clinical applications
- emergency CT triage
- incidental findings
Regulatory information
- Products described as 'FDA-cleared and/or CE-marked (MDR)' source
As stated by the company or the cited regulator page. Authorisation is specific to a product, version and market; confirm the current status with the company and the regulator. About medical device regulation
Official sources
Last verified 29 October 2026 against the sources above.
Questions about Avicenna.AI
How is radiology AI different from general diagnostic imaging software?
General diagnostic imaging software stores, displays and manipulates images, for example PACS viewers, 3D reconstruction or measurement tools operated by the user. Radiology AI adds automated analysis that produces its own output, such as a detection, triage flag or quantitative result.
The distinction matters for evaluation:
- Viewing and processing tools are judged mainly on image quality, usability and integration.
- AI analysis tools also need evidence on accuracy, populations studied, failure modes and monitoring.
Both can be regulated medical devices depending on intended use; in the US many image management and processing functions fall under device classifications. Many vendors now combine the two, so ask which functions are AI-based and which are covered by a regulatory authorization.
What is AI image reconstruction and denoising?
AI image reconstruction and denoising use deep learning to produce clearer images from raw scanner data or to reduce noise and artifacts, for example in CT or MRI. Vendors often position these tools as supporting lower-dose or faster scanning protocols.
Unlike detection software, these tools change the image the clinician reads rather than adding a finding. Questions to ask include:
- Is the function built into the scanner or a separate software product?
- Which scanners, protocols and body regions is it authorized for?
- How was diagnostic image quality evaluated, and could fine detail be lost or altered?
These functions are typically regulated as part of the imaging device or as image processing software, depending on the product and market.
What is AI triage or worklist prioritization in radiology?
AI triage software analyzes incoming imaging studies and flags those that may contain a time-sensitive finding so clinicians can review them sooner, for example by moving a study up the worklist or sending a notification.
In the US, FDA has a device classification for radiological computer-aided triage and notification software (21 CFR 892.2080). As described in that regulation, such a device notifies clinicians of time-sensitive images, does not mark the finding on the original image, does not remove cases from the reading queue, and operates in parallel with the standard of care.
Triage is therefore not a diagnosis: every study still needs to be read. When evaluating triage tools, ask about the target findings, notification workflow and how missed cases are handled.