The Good Tech Companies - How to Build an AI Medical Scribe With AssemblyAI

Episode Date: March 9, 2026

This story was originally published on HackerNoon at: https://hackernoon.com/how-to-build-an-ai-medical-scribe-with-assemblyai. Building a medical scribe requires more than transcription accuracy. It's about creating a system that fits into clinical workflows while respecting privacy. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #ai, #medical-ai, #voice-assistant, #assemblyai, #deployable-healthcare-solution, #python, #ai-medical-scribe, #good-company, and more. This story was written by: @assemblyai. Learn more about this writer by checking @assemblyai's about page, and for more stories, please visit hackernoon.com. Building a medical scribe requires more than transcription accuracy. It's about creating a system that fits into clinical workflows while respecting patient privacy.

This audio is presented by Hacker Noon, where anyone can learn anything about any technology. How to build an AI medical scribe with Assembly AI by Assembly AI. AI medical scribes are transforming healthcare documentation, but building one that works in clinical settings requires more than basic transcription. You need accurate medical terminology capture, reliable speaker identification, and privacy safeguards that protect patient data. This tutorial walks you through building a functional AI medical scribe in Python. You'll start with basic transcription and progressively add speaker identification, PE redaction, soap note generation, and automatic data deletion. By the end, you'll have a working prototype

that handles real-world clinical scenarios. What makes a production-ready medical scribe, before diving into code, understand what separates a proof of concept from a deployable healthcare solution. Essential features checklist feature why it matters implementation complexity, high accuracy on medical terms misheard drug names or dosages create clinical risks medium. Requires domain-specific model speaker identification distinguishes doctor from patient for proper documentation low. Handled by speech recognition API PII redaction removes names, dates, and identifiers to protect privacy medium. Needs careful configuration soap note generation converts conversations into structured clinical documentation medium. Requires LLM integration automatic data deletion ensures patient

data doesn't persist on third-party servers low, straightforward API calls step one. Basic transcription setup. Start with a simple transcription request. This establishes the foundation before layering on medical-specific features. Step two. Adding speaker identification. Medical documentation requires knowing who said what. Was it the doctor prescribing medication or the patient requesting it? Speaker diarization automatically separates different speakers in the conversation. Assemblyize speaker Diorization identifies when different people are speaking and labels them as Speaker A, Speaker B, and so on. To separate speakers further, we can use assembly eyes separate speaker identification feature toe match specific names to speakers. Here, we'll map these speaker labels to Roles like,

Doctor, and Patient. The speaker Diorization works best when each speaker talks for at least 30 seconds uninterrupted, though the model handles real-world conversations with cross-talk and short phrases. Step 3. Improving medical terminology accuracy with word boost and protecting patient privacy with PE redaction. A common problem. Doctors prescribe tramadol, but transcription shows tramadol in lowercase. Small formatting issues like this matter in medical records where precision is critical. Assemblyi's word boost feature lets you bias the model toward specific terms and their proper formatting. You provide a list of medical terms, drug names, or procedures, and the model, medical conversations also contain protected health information, phi, patient names, birth dates,

social security numbers, addresses. If this data appears in your transcripts and you store them on third-party servers, you're potentially violating privacy regulations. Assemblyize PE redaction automatically identifies and removes personally identifiable information from transcripts. You can configure what gets redacted and how. Step 4. Generating soap notes with LLM Gateway. Raw transcripts are useful, but doctors need structured documentation. Soap notes, subjective, objective, assessment, plan, are the clinical standard for organizing patient encounters. This is where large language models come in. Assemblyize LLM gateway provides a unified interface to various LLMs. Call it with your existing assembly AIAP Ike. The LLM processes the conversation and outputs structured

clinical notes. The doctor gets documentation ready for the EHR without manual transcription. Step 5. Automatic data deletion. The most reliable way to protect patient data is to ensure it doesn't persist on third-party infrastructure. After retrieving your transcript and generating soap notes, delete the transcript from AssemblyI servers. Data retention for LLM Gateway when using LLM Gateway with an executed business associate agreement, BAA, and Anthropic or Google inference models, assembly AI provides zero data retention for inputs and outputs. The LLM Gateway Processes requests ephemerally. Your transcript text and the generated soap notes are not stored beyond the immediate API call.

Only minimal metadata is retained for logging and billing purposes. Time to live FOR transcript TS for additional protection of transcript data. Assembly AI offers Time to Live, TTL, settings. As of November 26, 2024, customers with assigned business associate agreement, BAA, automatically have a three-day TTL applied to all. transcripts. This TTL is subject to change. Transcripts automatically delete after this period, even if you forget to send the delete request manually. Putting it all together, here's the complete implementation with all features enabled. Why this approach works for medical AI. Building a medical

scribe requires more than transcription accuracy. It's about creating a system that fits into clinical workflows while respecting patient privacy. This implementation handles the real-world challenges, medical terminology accuracy through word boost, clear speaker attribution with speaker diarization, privacy protection via PE redaction and automatic deletion, structured output through LLM generated soap notes, data minimization by removing all traces from third-party servers, the result, a functional prototype you can test with clinical audio. From here, add features like real-time transcription for live consultations, integration with EHR systems, or custom system soap note templates for different specialties. This implementation addresses the core challenges

of medical AI, terminology accuracy through word boost, clear speaker attribution with diarization, privacy protection via PE redaction and automatic deletion, and structured output through LLM generated soap notes. The combination creates a foundation for clinical grade medical scribes that respect patient privacy while delivering documentation that fits into real healthcare workflows. From here, add real-time transcription, EHR integration, or specialty-specific templates. Next steps. Want to build on this foundation? Check out these resources. AssemblyI's speech to text documentation, P-E redaction policies and configuration, LLM Gateway for Medical Use Cases. If you're building a healthcare AI product and need help with compliance, accuracy benchmarks,

or production deployment, the Assembly AI team has experience with medical AI companies and can help navigate technical and regulatory challenges. Thank you for listening to this Hackernoon story, read by artificial intelligence. Visit Hackernoon.com to read, write, learn, and publish.