Medical Transcription: AssemblyAI vs Deepgram (2026)

The hottest vertical in Voice AI right now isn't customer support—it's Healthcare.

"Ambient Scribes" (AI tools that listen to doctor-patient conversations and automatically generate SOAP notes) are exploding in popularity. If you are building one, the most critical decision you will make is choosing the underlying Speech-to-Text (STT) engine.

The two heavyweights are AssemblyAI and Deepgram. But unlike general transcription, medical audio has zero margin for error. A missed "not" or a confused drug name can be dangerous.

Here is how they stack up for medical use cases in 2026.

1. Medical Accuracy (The "Drug Name" Test)

General models often fail on complex terminology like "Ondansetron" or "Hyperlipidemia".

AssemblyAI

AssemblyAI offers a specialized Medical Speech-to-Text model (based on Universal-2 architecture but fine-tuned on petabytes of medical data).

• Strengths: It understands brand names vs. generic drug names, medical conditions, and shorthand used by doctors.
• Formatting: Excellent at handling lists and structured dictation.

Deepgram Nova-3

Deepgram's Nova-3 is a general-purpose beast. While it's incredibly smart, it lacks a dedicated "Medical" model endpoint in the same way AssemblyAI positions theirs.

• Performance: Surprisingly good on common medical terms due to its massive training set.
• Weakness: Can struggle with very specific oncology or cardiology jargon compared to AssemblyAI's specialized model.

Winner: 🏆 AssemblyAI. For pure medical accuracy, their specialized model is the industry gold standard.

2. PII Redaction & HIPAA Compliance

Both providers are HIPAA compliant (if you sign a BAA). But how they handle Protected Health Information (PII/PHI) differs.

• AssemblyAI: Offers "Entity Detection" that can automatically identify and redact names, SSNs, dates, and addresses before the text leaves their secure environment. Their entity recognition is context-aware (knowing that "Parkinson" is a disease here, not a person's name).
• Deepgram: Offers redaction features, but they are often regex-based or less contextually nuanced than AssemblyAI's NLP layer.

Winner: 🏆 AssemblyAI. Their "Audio Intelligence" suite makes redaction safer and easier to implement.

3. Latency (Real-time vs. Post-visit)

• Deepgram: The undisputed king of speed. If your app displays a live transcript on the doctor's screen as they talk, Deepgram is the only viable choice (<300ms latency).
• AssemblyAI: Slower. But for ambient scribes, does speed matter? Usually, the note is generated after the visit. The doctor doesn't need to see the text instantly; they just need the final note to be perfect.

Winner: 🏆 Deepgram (for speed), but Tie (for actual utility in this use case).

4. The "Wildcard": Soniox

A query we often see is about Soniox. Soniox is a smaller player that focuses entirely on "few-shot" learning.

• The Pitch: You can teach Soniox new words (like a new experimental drug name) instantly by providing a context list.
• Reality: While impressive, they lack the scale and reliability of Deepgram or AssemblyAI. Use them only if you have a very specific niche vocabulary that the big models fail on.

Conclusion: Which One for Your Scribe?

• Go with AssemblyAI if: You prioritize accuracy above all else. You want the AI to handle PII redaction and complex medical formatting out of the box. The slightly higher cost ($0.006/min) is worth the reduced risk of medical errors.
• Go with Deepgram if: You are building a real-time assistant that needs to nudge the doctor during the visit (e.g., "You forgot to ask about allergies"). The speed of Nova-3 enables real-time intervention.

Our Recommendation: For 90% of Ambient Scribes, AssemblyAI is currently the safer bet.

HIPAA and BAA: What to Verify Before Sending PHI (2026)

Both vendors will sign a BAA — that part is table stakes. What differs is what happens to PHI inside the pipeline.

Dimension	AssemblyAI	Deepgram
BAA	Available; SOC 2 Type II	Available; HIPAA-compliant enterprise tier
Clinical model	Medical Mode on Universal-3.5 Pro (one parameter)	Nova-3 Medical (separate model route)
Published missed-entity rate	3.2% (vendor-published, lowest benchmarked)	Not published
Async clinical pricing	$0.36/hr ($0.21 base + $0.15 Medical Mode)	Per-minute tiers, varies by commit
Streaming clinical pricing	$0.60/hr ($0.45 + $0.15)	Available, latency varies by config
PHI redaction	Entity-aware (knows "Parkinson" the disease vs the name), across audio and transcripts	Redaction available, largely pattern-based

Pre-launch checklist for scribe builders

1. Sign the BAA first. No PHI in test traffic before the paperwork exists — including your own dogfood recordings.
2. Redact before you store. Prefer a pipeline where names, dates and SSNs are masked before transcripts hit your database or logs.
3. Measure missed entities, not just WER. A 7% WER with perfect drug-name capture beats a 5% WER that mangles dosages. Test on your specialty's vocabulary (oncology ≠ cardiology).
4. Log who accessed what. BAA + audit trails + encryption at rest is the minimum viable compliance posture.

Pricing and benchmark figures from vendor pages/blogs, September 2026 — verify current terms before signing.

Related comparisons & reviews

• Deepgram vs. AssemblyAI (2026): Which STT API Should You Choose?
• Deepgram Nova-3 Review 2026: Speed, Accuracy & Pricing
• Deepgram Flux vs. VAD: Why Turn Detection is the New Latency Battlegro