Medical AI company
Aidoc
Clinical AI company offering an enterprise platform (aiOS) and imaging algorithms for radiology, neurovascular, cardiology and vascular care.
Company overview
Aidoc develops clinical AI that flags and prioritises findings on medical imaging and coordinates care teams. Its aiOS enterprise platform runs Aidoc and partner algorithms inside existing hospital IT. Solutions cover radiology triage, stroke and brain aneurysm, coronary artery disease, VTE and aortic conditions.
- Company type
- AI Technology Developer, Platform Provider
- Deployment
- enterprise platform integrated with hospital IT
In its own words: “Aidoc delivers clinical AI that empowers healthcare providers with faster, more accurate decision support.” source
AI products and platforms
- aiOSAI-powered
Enterprise platform for deploying and integrating clinical AI across a health system
Product page ↗ - Radiology solutionsAI-powered
AI that prioritises imaging findings and activates care teams
Product page ↗
Technology categories
Clinical applications
- imaging triage
- care coordination
- incidental findings follow-up
Regulatory information
- Aidoc's site states it holds 17 FDA clearances ('Most FDA-clearances in clinical AI (17)') 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 Aidoc
How is AI used with ECGs from wearables and remote monitors?
AI is used to screen large volumes of ECG data from patches, wearables and remote monitors, flagging possible arrhythmias or recordings that need clinician review. This helps manage the large amount of data produced by continuous monitoring.
Important distinctions for buyers:
- A consumer feature on a wearable and a clinical-grade analysis service can have very different intended uses and authorizations.
- Authorization often covers specific recording hardware, leads and populations.
- Signal quality and motion artifacts affect results.
In the US, software that analyzes physiological signals does not meet the criteria for non-device CDS. Confirm each product's status, and check how alerts are routed to clinicians and documented.
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.