Solutions · Medical imaging
AI‑assisted image analysis that helps radiologists prioritise critical cases, detect findings earlier and handle growing study volumes — deployed on sovereign infrastructure in Moldova.
Moldova performs an estimated 350,000–400,000 CT, MRI and PET‑CT studies per year — roughly 1,100 to 1,600 every day. Radiologists face growing worklists, and in time‑critical conditions such as stroke every minute between scan and treatment decision matters. A study that waits in a queue for hours can mean a worse outcome for the patient.
AI models analyse incoming studies as soon as they are acquired and flag suspected critical conditions — such as signs of stroke — to the responsible clinician, with a target turnaround of 5–10 minutes. Flagged studies move to the top of the radiologist’s worklist.
Models for lung nodule detection, organ and lesion segmentation and quantitative measurements support radiologists in routine reading, making reports more consistent and follow‑up easier to track.
Automated registration and fusion of PET and CT images supports oncology staging and treatment‑response assessment.
Models are validated and, where appropriate, further trained on Moldova’s own historical imaging archives, so their performance reflects local equipment, protocols and patient populations.
FAQ
No. It supports them by prioritising urgent studies and highlighting findings. Diagnosis and reporting remain the radiologist’s responsibility.
Yes, the solution connects through standard DICOM interfaces to existing scanners and PACS, independent of the scanner manufacturer.
Models are validated on local data before clinical use and are monitored continuously afterwards. New versions go through a separate testing environment before deployment.
Further reading
AI in radiology now has randomised-trial evidence in at least two areas, breast screening and stroke workflow, and a much thinner base elsewhere. Here is what the strongest studies show, what they do not, and how to deploy the tools responsibly.
Capacity for national healthcare AI is not one number. It is the sum of several workloads with different shapes, plus redundancy and room to grow. Here is a step-by-step way to plan it.
Estonia is the usual reference point for national e-health. Its success came less from any one application than from legal duties, digital identity and a shared data exchange layer put in place years before the health record. Here is what it built, in what order, and what Moldova can reuse.
Talk to our team about specification, supply, deployment and support — from a single hospital department to a national programme.