February 25, 2026

How do I build an AI medical scribe using speech-to-text?

Kelsey Foster

Growth

Medical

Reviewed by

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Building an AI medical scribe requires solving complex technical challenges that go far beyond basic speech recognition. You need specialized systems that handle medical terminology, process multiple speakers accurately, and maintain the precision required for clinical documentation where errors can directly impact patient care.

This guide walks you through the complete technical architecture—from capturing audio in noisy clinical environments to generating structured SOAP notes that integrate seamlessly with your EHR system. You'll understand the specific requirements for medical-grade speech-to-text, the role of Large Language Models in clinical note generation, and the critical compliance considerations that separate healthcare applications from general transcription systems.

What is an AI medical scribe?

An AI medical scribe is software that listens to your patient conversations and automatically creates medical notes. This means it captures everything said during the appointment and turns it into structured documentation like SOAP notes without you having to type anything.

The system works in the background while you focus on your patient. Instead of looking at a computer screen or writing notes, you can maintain eye contact and have natural conversations while the AI handles all the documentation.

These systems connect directly to your Electronic Health Record (EHR) so the notes appear in the patient's file automatically. You just need to review and approve them before they become part of the official medical record.

How do AI medical scribes work?

Building an AI medical scribe requires four connected steps that work together. If any step fails, your entire system breaks down.

Step	What Happens	Technology Used	What You Get
Audio Capture	Records conversation	Microphone, audio software	Raw audio file
Speech-to-Text	Converts speech to words	Medical speech recognition	Text transcript
Clinical Processing	Finds medical information	Natural language processing	Organized medical data
Note Creation	Makes formatted notes	Clinical templates	SOAP notes for EHR

Let's break down each step so you understand what's actually happening.

Speech-to-text transcription

Speech-to-text systems convert your conversations into written words as you're speaking. This means the text appears in real-time, not after the appointment ends.

Your medical environment creates unique challenges that regular speech recognition can't handle:

Medical terms: Words like "hydroxychloroquine" or "esophagogastroduodenoscopy" that normal systems completely butcher
Multiple voices: The system needs to tell the difference between you, your patient, nurses, and family members
Background noise: Medical equipment, hallway chatter, and air conditioning all interfere with audio

Regular speech-to-text models trained on everyday conversations will fail catastrophically with medical content. When you say "prescribe metformin 500mg," a general model might write "met for men 500 message"—creating dangerous errors in your documentation.

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Medical terminology and jargon recognition

Medical speech recognition needs special training because healthcare uses thousands of unique words that don't exist in normal conversation. When you say "patient presents with intermittent claudication," every word in that phrase requires specialized medical training to recognize correctly.

Drug names create the biggest problems. Here's what goes wrong with regular systems:

"Celebrex" becomes "Celexa"—completely different medications
"Zantac" turns into "Xanax"—one treats heartburn, the other anxiety
"Clonidine" gets confused with "Klonopin"—blood pressure versus seizure medication

Your AI medical scribe must also handle medical abbreviations correctly. When you say "BP one-forty over ninety," it needs to write "blood pressure 140/90" in proper medical format.

Speaker identification and diarization

Speaker diarization is technology that figures out who's talking at each moment. This matters because you need to know whether symptoms came from the patient or observations came from you.

The system learns voice patterns to keep track of different speakers even when people interrupt each other. When your patient says "my chest hurts when I breathe" and you respond "I hear crackling in your left lung," the AI must correctly label who said what.

This gets complicated when nurses pop in to report vitals or family members provide medical history. Without accurate speaker separation, critical information gets misattributed in your final notes.

Clinical note structuring with LLMs

Large Language Models (LLMs) take your conversation transcript and organize it into proper SOAP note format. The LLM pulls out symptoms, physical exam findings, diagnoses, and treatment plans from natural conversation flow.

Here's how it works. This conversation: Patient: "I've had this headache for three days. Mostly on the right side." Doctor: "Any nausea or vision changes?" Patient: "Some nausea, yes. No vision problems." Doctor: "Your blood pressure is 150/95. This looks like a tension headache related to your hypertension."

Becomes this organized note:

Subjective: Patient reports 3-day right-sided headache with nausea, denies vision changes
Objective: BP 150/95, elevated
Assessment: Tension headache, likely hypertension-related
Plan: [Based on your treatment discussion]

You still need to review and edit these AI-generated notes. The technology assists but doesn't replace your clinical judgment.

EHR system integration

The final step pushes your structured notes directly into your Electronic Health Record through API connections. This eliminates copying and pasting between different systems.

Each EHR expects information formatted differently. Your AI needs to format notes for Epic differently than Cerner or other systems. Successful integration means you click one button to transfer the AI's documentation into your patient's official record.

What are the technical requirements for medical speech-to-text?

Medical transcription needs much higher standards than regular speech recognition because mistakes can harm patients. A wrong medication name or incorrect dosage could be dangerous.

Requirement	Medical Speech-to-Text	Regular Speech-to-Text
Accuracy	Must be extremely precise on medical terms	General accuracy is fine
Vocabulary	100,000+ medical words	30,000 common words
Speed	Under 300ms delay	1-2 second delay okay
Compliance	Needs healthcare agreements	No special requirements
Speaker ID	Critical for correct attribution	Nice to have

Medical transcription accuracy standards

Your medical documentation needs near-perfect accuracy on clinical terms because errors directly affect patient care. A transcription that changes "15mg" to "50mg" could cause a dangerous overdose. Confusing "hypertension" with "hypotension" completely reverses the diagnosis.

You need speech-to-text systems that demonstrate accuracy on medical benchmark datasets before you use them. These tests check recognition of drug names, body parts, procedures, and medical abbreviations for your specific specialty.

Real-time streaming processing

You need streaming transcription that processes speech as it happens, not batch processing that waits until conversations end. Real-time feedback lets you see if the AI is capturing information correctly so you can repeat important details if needed.

Streaming must work under 300 milliseconds—any longer and you'll notice the delay disrupting your workflow. The technical challenge involves processing audio chunks while remembering context from earlier in the conversation.

Batch processing won't work for medical workflows. You can't wait until after appointments to discover the AI missed critical information.

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Medical vocabulary and pharmaceutical recognition

Your medical speech recognition must handle specialized vocabulary including:

Drug names: Over 20,000 FDA-approved medications plus brand names and generics
Body parts: Thousands of anatomical terms and descriptions
Procedures: Medical procedure names and CPT codes
Abbreviations: Standard shortcuts like "PRN" (as needed) or "BID" (twice daily)

Sound-alike medications create the biggest challenges. "Hydralazine" and "hydroxyzine" sound nearly identical but treat completely different conditions—blood pressure versus anxiety.

AssemblyAI's Universal-3-Pro model can be adapted for high accuracy on medical data using the `prompt` and `keyterms_prompt` parameters. The "Medical Scribe Best Practices" documentation details this approach for domain-specific adaptation.

What are the key challenges in building medical AI scribes?

Building effective medical AI scribes means solving problems that don't exist in regular transcription. These challenges directly determine whether your technology helps or hurts clinical workflows.

Clinical audio environment challenges

Hospitals and clinics create acoustic nightmares for speech recognition. Medical equipment generates constant noise—ventilators, monitors, infusion pumps all make sounds that interfere with your conversations.

These environmental factors can cut transcription accuracy by 20-30% compared to quiet rooms. You need technical solutions like:

Noise filtering: Removes consistent background sounds like air conditioning
Directional microphones: Focus on voices while ignoring ambient noise
Medical environment training: Models trained specifically on noisy hospital recordings

Even with these fixes, emergency departments and intensive care units remain challenging where background chaos competes with critical medical discussions.

AI hallucinations and transcription errors

AI hallucinations happen when speech recognition or language models generate fake but believable medical information. Your system might add symptoms never mentioned or create medication names that don't exist.

Common problems include:

Phantom symptoms: Adding "chest pain" when the patient only said "shortness of breath"
Made-up medications: Creating drug names by mixing syllables from real medications
Guessed dosages: Inventing standard doses when amounts weren't clearly stated

You need confidence scoring that flags uncertain text for review rather than guessing. But completely eliminating hallucinations remains an unsolved problem in AI development.

This is why you must review all AI-generated notes—the technology assists but can't replace clinical oversight.

Final words

AI medical scribes depend entirely on accurate speech-to-text as their foundation. The whole system—from capturing conversations to creating notes—fails without reliable medical speech recognition that handles specialized vocabulary, processes speech in real-time, and maintains high accuracy on clinical terms.

AssemblyAI's core transcription models, like Universal-3-Pro, can be specialized for medical documentation using prompting features to handle complex medical vocabulary. AssemblyAI enables covered entities and their business associates subject to HIPAA to use AssemblyAI services to process protected health information through Business Associate Agreements, supporting developers who need reliable speech-to-text infrastructure for healthcare applications.

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FAQ

What accuracy level do you need for medical speech-to-text?

Medical applications need near-perfect accuracy on clinical terminology because documentation errors can directly impact patient treatment decisions, requiring much higher standards than general transcription systems.

Can you use open-source speech models for medical scribes?

Open-source models typically lack medical vocabulary training and healthcare compliance features, making them unsuitable for clinical documentation without extensive customization and regulatory approval.

How do you ensure HIPAA compliance when building medical transcription software?

You must use speech-to-text providers that offer Business Associate Agreements and implement proper technical safeguards for protected health information, including encryption and detailed audit logging.

What specific technical challenges make medical transcription difficult?

Medical transcription faces unique obstacles including recognizing complex pharmaceutical names, processing multiple speakers accurately, maintaining sub-300ms streaming latency, and achieving near-perfect accuracy in noisy clinical environments.

Do speech models need special training for medical terminology?

General models are adapted for medical use. The `Medical Scribe Best Practices` documentation shows how to use `keyterms_prompt` with `Universal-3-Pro` to boost accuracy for specific medical terms.

How do AI medical scribes separate different speakers during appointments?

Medical scribes use speaker diarization technology to identify distinct voices—patients, doctors, nurses, family members—ensuring symptoms and clinical observations get correctly attributed in the final documentation.

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