Closing the gap between the scan and the report, using artificial intelligence to widen access to timely, expert radiology. DiagnovisAI detects, quantifies and localises findings, and drafts a structured report for a radiologist to review and sign.
Imaging demand is rising across public and private providers, driven by trauma, tuberculosis and other pulmonary disease, stroke, oncology and obstetric indications, while reporting capacity is not. Uganda shows the gap at its sharpest.
Artificial intelligence is the most frequently proposed answer to that gap. It is also already in use. Detection, quantification, localisation, triage and structured draft reporting are working products, and DiagnovisAI is one of them. Five things it does, in the order a radiologist meets them.
Reporting delay runs across every modality and every indication. We began with two studies, chosen for where volume and urgency meet. A crash brings you in within the hour; a cough brings you in within the year. Both wait for the same reader.
Chest radiography carries the largest share of imaging demand. It also carries the widest range of findings per image, covering trauma, infection and chronic disease.
Uganda’s national tuberculosis screening evidence found it substantially more sensitive than screening on symptoms alone, because a large share of people with active TB have no symptoms to report. It is the study that finds disease nobody came in for.
Motorcycle crashes have made head injury one of the defining emergency presentations in Ugandan hospitals, and trauma patients arrive fast. A bleed that is expanding needs a decision in hours, not overnight.
In stroke, patients often reach hospital days after onset. The read still decides everything that follows. Telling a bleed from a clot changes the treatment, however late the patient arrives.
A walkthrough of DiagnovisAI with our team, built around your imaging workflow.