Medical transcription API: Scribe v2 Medical GA, and what Sume STT is

ElevenLabs reports Scribe v2 Medical as GA on 2026-09-11. Sume's STT is general speech-to-text with word timings, not a medical product. Where that matters.

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If you need transcription tuned for clinical speech, Sume's speech-to-text is not that product. A changelog roundup reports that ElevenLabs made Scribe v2 Medical generally available on 2026-09-11. Sume STT is a general transcription job: you give it a public HTTPS audio URL and get back text, word timings and an optional language hint. Its docs make no medical, clinical-vocabulary or health-data compliance claim, so do not assume one.

Use this page to decide quickly whether Sume STT can serve your case at all. Sume also does not publish accuracy figures for clinical speech in the docs read here, so there is nothing to compare against ElevenLabs' medical model.

What does Sume STT do?

A job takes audio_url, an optional language_code (omit it for auto-detect), and an optional duration_seconds between 1 and 600 that improves the usage reservation. Word timings are always returned, and segmentation with sentence mode adds sentence segments. Provider settings such as diarization and audio-event tagging are fixed on the server, so you cannot switch speaker labels on or add a custom term list through the request.

Fit for medical use, from the Sume OpenAPI schema and Daily AI Pedia, read 2026-10-02.
NeedSume STTWhere to look
Clinical vocabulary tuningNot documentedA medical-specific vendor
Health-data compliance statementsNot made in the docsSume's terms, and your counsel
Speaker labelsNot settable in the requestA tool with diarization controls
Word timingsAlways returnedResult words[]
Audio length per jobUp to 600 seconds reservedSplit longer audio

When is general STT still fine?

For non-clinical speech near a clinic: a marketing video with a doctor, a patient-education explainer, an internal training recording, or captions for a conference talk. Check every transcript yourself before publishing, because a general model may misspell drug names and anatomy. Do not feed patient recordings into any service until your privacy review says you may. A useful test is to ask whether a wrong word would change a decision; if it would, a human must review the transcript, whichever tool made it.

What should I do?

Write down the one requirement you cannot waive: a vocabulary, a compliance statement or speaker labels. If any of them applies, shortlist a medical-specific tool and ask for its terms. If none applies, run a ten-minute sample through Sume STT and proofread it. The API reference lists the STT request fields, and Jobs and results shows how to read the result.

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