Medical reasoning
A patient describes symptoms. FR-OS evaluates 30,981 diagnoses against formally proven rules and returns a ranked differential with exclusion certificates showing why each diagnosis was ruled in or out. Every determination is auditable.
See it in action
A patient describes crushing chest pain, sweating, and nausea. FR-OS identifies the clinical findings, evaluates all candidate diagnoses, and returns a ranked differential. No physician input required.
By the numbers
Each with formally verified evaluation rules derived from medical knowledge bases.
Validated against real clinical scenarios. The correct diagnosis appears in the differential.
The engine is logically precise by construction. Every exclusion is a proof, not a statistical estimate.
How it differs
Neural models produce ranked lists with probability scores. "Myocardial infarction: 73% likely." There is no explanation of why, no record of what was considered, and no guarantee the model won't produce a different answer tomorrow.
The engine evaluates each diagnosis against the patient's findings using formally verified rules. Diagnoses are ranked by how much evidence supports them. Each exclusion comes with a certificate showing exactly which findings ruled it out. Deterministic, auditable, reproducible.
Disambiguation in medicine
Medical language is full of ambiguity. "Cold" can mean temperature or illness. "Discharge" can mean release from hospital or bodily fluid. "Positive" can mean good news or a concerning test result.
FR-OS resolves these ambiguities before clinical reasoning begins. The same engine that achieves 88.4% on standard NLP benchmarks processes clinical text, ensuring that downstream diagnosis operates on meanings, not words. This eliminates an entire class of errors where ambiguous terms match the wrong diagnoses.
Early access
Be the first to know when FR-OS launches. We'll notify you when API access is available.