Hospitals & Referral Centres
Referral hospitals carry the heaviest imaging loads — often with the thinnest radiology cover.
Demand concentrates where specialists are scarcest
With roughly one radiologist per 500,000 people in Uganda, imaging demand pools at referral centres faster than reporting capacity can follow.
The result is structural: worklists grow, reads queue, and referrers wait on the reports they need to act.
Structured assistance inside your workflow
DiagnovisAI drafts organized observations for every study it processes, giving your radiology team a structured starting point on each case — while every report still requires a radiologist's review and sign-off.
Referring teams receive reports in one consistent four-section format: Technique, Findings, Impression, Recommendations.
How engagements begin
We start with a conversation with your imaging department — understanding your modalities, volumes, and reporting workflow — and evaluate fit before any clinical use. [Placeholder — engagement process details.]
Radiologists & Clinical Teams
A 1:500,000 ratio means every radiologist carries an impossible workload. DiagnovisAI is built to lighten it — not to second-guess the expert.
Drafts, not verdicts
DiagnovisAI organizes its analysis into a structured draft, so review begins from something ordered rather than from a blank page.
You read the images, judge the draft, edit freely, and decide — exactly as you would with a registrar's preliminary report.
Structure comes first
Technique, Findings, Impression, Recommendations — the report's skeleton is always in place, so your time goes to interpretation rather than formatting.
Your judgment, final word
Nothing the platform produces reaches a referrer without your review and sign-off. DiagnovisAI works for you — it does not work around you.
Shaped by the people who read
We develop DiagnovisAI in conversation with practicing radiologists and clinical teams, and we evaluate it under formal clinical validation. [Placeholder — clinician advisory details.]
Health Systems & Public Health
A one-in-500,000 specialist ratio is not a staffing problem any single hospital can hire its way out of. It is a system problem.
Extend the specialists you already have
Training a radiologist takes years. Assistive AI offers a nearer-term lever: structured support that helps each existing specialist's judgment reach further across the system.
Every interpretation remains radiologist-led — the leverage comes from removing everything around the judgment, not the judgment itself.
One reporting standard across facilities
A single four-section report structure — Technique, Findings, Impression, Recommendations — brings consistency to imaging reports across sites and levels of care.
Closing a measurable gap
The radiologist shortage is quantified and visible. DiagnovisAI is built to narrow it where it is widest — beginning in Uganda, with the public system in view.
Regional ambition
Our roadmap includes expansion across East Africa. This is an aspiration, not a current deployment — and we will pursue it on the strength of evidence built at home first.
Talk to us about system-level deployment
Ministries, agencies, and health-system planners — we would like to understand your imaging landscape.
Request a Demo