We believe healthcare AI should be verifiable. We publish our benchmarks, open-source our evaluation code, and release models under permissive licences.
An on-device 0.6B medical speech-to-text model, benchmarked against 21 open and closed systems.
42 speech-to-text models ranked on medical conversations using Medical Word Error Rate.
A safety-first SOAP benchmark measuring hallucinations, evidence grounding, and clinical coverage.
An open 3B clinical model for structured SOAP notes, released under the MIT licence.
Omi Med STT v1 model weights — on-device medical speech-to-text. CC-BY-4.0.
HuggingFace → omi-med-stt-runtimeRuntime CLI for Omi Med STT v1 — MLX, NeMo, and parakeet.cpp backends. MIT licence.
GitHub → medical-STT-evalEvaluation framework for speech-to-text models on medical conversations.
GitHub → medical-note-evalSOAP note safety benchmark for hallucination, grounding, and quality.
GitHub → sum-smallOmi-Sum 3B model weights and training dataset. MIT licence.
HuggingFace →