Quick answers for healthcare builders
What is a clinical speech recognition API?
A clinical speech recognition API converts healthcare audio into text for a software product. The useful ones add medical vocabulary, timestamps, speaker labels and privacy controls—and make it possible to test clinical terms, drug names and dosages separately.
What is the best medical speech-to-text API?
There is no universal winner. On our sealed 1,513-clip medical benchmark, Omi had the lowest medical-term error and the highest dosage F1 among the tested systems. Still run a blinded test on your own accents, specialties and recording conditions before choosing a provider.
What word error rate should I expect?
Overall word error rate is only one signal. The leading systems on our benchmark were close to 6% overall WER, while their medical-term, drug-name and dosage errors differed. For healthcare, score those clinical failures separately.
Does a medical speech-to-text API support a BAA?
Some providers make BAA access plan-specific or sales-led. Omi lets you sign a BAA in the console on every hosted API plan, including Builder. Also compare retention, processing region and whether customer audio is used for training.
Which API is most accurate for clinical terms and drug names?
On our sealed benchmark, Omi recorded 0.94% medical-term error and 0.00% drug-name error. That is one benchmark, not a substitute for testing your own audio; the complete leaderboard and methodology are public.
What “best medical ASR” should mean
Speech recognition leaderboards usually report word error rate (WER): the percentage of inserted, deleted or substituted words. That is useful, but a clinical transcript can have a respectable WER while still changing a drug name or dose. The medical WER guide explains the scoring and test design in detail.
We therefore rank systems with three separate views: overall WER, medical-term WER (M-WER), and dosage-event F1. Drug-name error is reported independently as well. This makes the trade-off visible instead of hiding it inside one average.
1. Medical terminology
Use encounters that contain diagnoses, anatomy, abbreviations and medications. A dedicated M-WER score tells you how often the system changes the vocabulary clinicians care about. Do not infer medical accuracy from a general podcast or meeting benchmark.
2. Drug names and dosage
Drug-name error should be visible as its own metric. Dosage testing should align canonical number-and-unit events and report precision as well as recall, so invented doses are penalised rather than disappearing from the score.
3. Your actual workflow
Prerecorded consultation transcription, live dictation and far-field meetings are different products. Ask whether the published result covers your audio shape, language and speaker count. Test your own audio before signing a larger contract.
4. Privacy and control
For healthcare, check where audio is processed, how long it is retained, whether customer data is used for training, and how a BAA or DPA is signed. If audio must remain inside your environment, an open model or supported private deployment may matter more than a hosted benchmark lead.
5. Total product cost
Compare the actual configuration you need. Speaker diarization, vocabulary, language support, long-audio jobs and compliance may be bundled, metered separately or available only on enterprise plans.
Shortlist two or three systems using the benchmark, then run a blinded test on your own representative audio. Score medical terms and dosage before reviewing style or punctuation.
Best choices by product shape
There is no universal winner. A medical-first API, a general voice platform and a cloud-native service optimise different constraints.
- Medical-first hosted API: Omi packages medical vocabulary, speakers, timestamps and a self-serve BAA into every plan. See the complete pricing.
- Broad voice platform: Deepgram and AssemblyAI offer larger speech product catalogs. Compare the total feature configuration in Omi vs Deepgram and Omi vs AssemblyAI.
- Existing AI stack: OpenAI can reduce vendor count when speech already lives inside a broader OpenAI application. See the healthcare speech comparison.
- Existing cloud estate: Google Cloud and Azure can simplify identity and procurement. Compare the exact model and region, not only the vendor name.
- Audio must stay local: evaluate an open or supported private deployment. Omi's open medical edge model runs on Mac, CUDA and CPU.
Where Omi fits
Omi is designed for medical transcription and offers both a hosted API and an open edge model. The hosted API starts with 25 free audio-hours each month, includes a self-serve BAA, and is priced at $0.29 per batch audio-hour or $0.45 per live audio-hour beyond the pooled allowance.
Compare Omi with a specific provider, inspect the complete medical speech-to-text benchmark, read the HIPAA speech API checklist, evaluate speaker diarization for your clinical workflow, choose the hosted medical speech API, or run the open on-device medical speech-to-text model.