Medical AI company
Oxipit
Radiology AI developer, part of Sectra since 2026, offering chest X-ray, CT and MSK AI including autonomous reporting of normal chest X-rays.
Company overview
Oxipit develops AI applications for radiology covering chest X-ray, CT and musculoskeletal imaging. Its ChestEye product produces preliminary reports for chest X-ray findings, and ChestLink identifies normal chest X-rays and reports them autonomously in Europe. In 2026 the company became part of the Sectra group.
- Founded
- 2017 source
- Company type
- AI Technology Developer
In its own words: “Trusted worldwide, Oxipit, as part of the Sectra group, combines AI innovation with deep expertise in imaging workflows.” source
AI products and platforms
- ChestLinkAI-powered
Autonomous AI reporting of healthy chest X-rays (CE marked; not for autonomous use in the US)
Product page ↗ - ChestEyeAI-powered
AI generating preliminary reports for chest X-ray findings
Product page ↗ - CT SuiteAI-powered
AI for CT imaging
Product page ↗ - MSK SuiteAI-powered
AI for musculoskeletal imaging
Product page ↗
Technology categories
Clinical applications
- chest X-ray reporting
- normal study triage
Markets served
- Europe
Relationships
- Owned by Sectra source
Relationships are shown as stated on the cited page; the other party may not confirm them.
Regulatory information
- ChestLink: CE Class IIb-certified autonomous AI application for healthy chest X-ray reporting (April 2022) source
- Oxipit ChestEye received CE certification in 2019 source
- Autonomous operation is CE marked; not available for autonomous use in the United States 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 Oxipit
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.