From the source
Drug names are long, rare, and easy to mix up (hydroxyzine and hydralazine are one misheard syllable apart).
Vocabulary is dense, with dosages, units, anatomy, and pathology terms that can arrive in rapid sequence.
The stakes are also higher because a transcription error could end up in a patient’s chart.
That’s why ElevenLabs is today releasing Scribe v2 Medical , our Speech to Text model fine-tuned for clinical audio, which is now generally available on ElevenAPI .
Across the evaluations presented below, it consistently scores one of the lowest word error rates (WER) among the models compared, and reduces WER on clinical audio by approximately 35% compared to our base Scribe v2 model.
General purpose models are great at handling everyday speech, but can degrade where clinical workflows need them the most.
We trained a medical fine-tune of Scribe v2 focused on medication names along with clinical dictation, on public benchmarks anyone can reproduce.
Scribe v2 Medical is optimized for clinical use cases Clinical dictation (MedDictate) …
