Diagnify reasons like a senior clinician — every differential, dose and red flag grounded in evidence, never hallucinated. By voice or chat. In real time. Safe enough to put in front of a patient.
Generative AI hallucinates. In medicine, a confident wrong dose or a missed emergency is a sev-1. Diagnify is built the other way around — grounded first, generative second — so the dangerous failures are structurally impossible, not statistically unlikely.
Ranked differentials with Bayesian posteriors that update as evidence arrives — every diagnosis, test and dose traced to the medicines handbook, evidence base and Australian guidelines. Never free-generated.
Chest pain, a repeat script, a health check or 'I just feel off' — the engine reframes the reason and composes the right work-up: red flags → history → examination → investigations → plan. No templates.
A real-time spoken or typed intake that takes a full history, coaches safe self-examination, and screens danger — then hands a structured, grounded note to the doctor. The patient chooses how they speak.
An always-on red-flag screen pre-empts every turn; a groundedness gate verifies each specific against the record before it is ever spoken. A missed emergency or a hallucinated number is engineered out, not hoped against.
An always-on safety model reads every turn for life-threats — and pre-empts in the same breath.
The grounded engine builds the differential, the discriminating questions and the posterior probabilities.
A groundedness gate checks every spoken specific against the record. Pass → speak. Fail → defer.
A structured, sourced note lands with the clinician — live as it happens, or ready before the appointment.
“Most medical AI is a brilliant intern with no supervision.
Diagnify is the supervision.”
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