RadMatch
AIStudy-description matching for teleradiology & priors
A fast ML engine that maps free-text study descriptions to your exam codes — a lifesaver for teleradiology groups and for normalizing priors brought in from outside facilities. LLM fallback for hard cases, review queue for anything low-confidence.
What it does
RadMatch learns from your own code sets to translate inconsistent, free-text procedure descriptions into the correct standardized exam codes — the reconciliation headache every teleradiology group knows, and the mess that comes with priors pulled from outside facilities. Upload a code set, train a model, and let RadMatch resolve incoming descriptions in milliseconds, falling back to an LLM only when the statistical match is uncertain. Anything below a confidence threshold lands in a review queue so a human stays in the loop.
Capabilities
- check_circleNormalize priors brought in from outside facilities
- check_circleBuilt for teleradiology and multi-site reads
- check_circleTrain models directly from your uploaded code sets (CSV)
- check_circleDescription → exam-code matching in milliseconds, LLM fallback
- check_circleConfigurable confidence thresholds and review queue
- check_circleTerm overrides and custom mappings that persist across retrains
See RadMatch in your workflow
Request a walkthrough and pricing tailored to your environment.
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