Project context
Similar-Case Search for Medical Imaging Archives
AI search for medical imaging archives that finds similar cases from a single uploaded scan, so staff no longer depend on developers and database queries to find comparable studies.
Client identity kept confidential
The challenge
The solution
Technology stack
The result
The self-learning model put a case from the right spinal region at the top of the results in 98.5% of searches, against 73.5% for the conventional model. Of the top ten results, close to nine were relevant (86.9%), against half (49.9%) with the conventional approach. On a public medical imaging benchmark of 17,778 images, the same approach reached 99.7% top-1 accuracy.
That is where the business case gets real: an archive that was only reachable through developers becomes searchable for the people who actually work with the images, without labelling the archive first.
Next step
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Start with one concrete workflow. We assess the first agent, platform fit, and the most sensible route to production.
