Uploaded December 2025 | Updated September 2026, 1 week ago
🚀 Best practices for building ambient AI scribes: assemblyai.com/docs/medical-scribe-best-practices?utm_source=youtube&utm_medium=referral&utm_campaign=tutorials&utm_content=mart_ambient_scribe
Learn how to build a simple AI ambient medical scribe in Python using AssemblyAI.
In this tutorial, we start with a basic transcription request in under 30 lines of code and incrementally add features that matter in real clinical workflows. You’ll see how to improve medical transcription accuracy with key terms, label speakers by role (doctor vs. patient), generate SOAP notes using LLMs, and apply PII redaction to protect patient privacy.
We’ll also cover how to delete transcription and LLM data after processing so your application meets strict data retention and privacy requirements. By the end, you’ll have a practical foundation for building a production-ready ambient medical scribe.
Timestamps:
00:00 – Intro: Why accuracy and privacy matter for AI medical scribes
00:40 – Building a basic transcription pipeline in Python
01:01 – Speaker identification (doctor vs. patient)
02:18 – Generating SOAP notes with LLMs
02:56 – PII redaction and data privacy
03:40 – Deleting data and retention best practices
04:57 – Final recap
▬▬▬▬▬▬▬▬▬▬▬▬ CONNECT ▬▬▬▬▬▬▬▬▬▬▬▬
🖥️ Website: assemblyai.com
🐦 Twitter: twitter.com/AssemblyAI
👽 Reddit: reddit.com/r/assemblyai
▶️ Subscribe: youtube.com/c/AssemblyAI?sub_confirmation=1
🔥 We're hiring! Check our open roles: assemblyai.com/careers
▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬
🚀 Best practices for building ambient AI scribes: assemblyai.com/docs/medical-scribe-best-practices?utm_source=youtube&utm_medium=referral&utm_campaign=tutorials&utm_content=mart_ambient_scribe
Learn how to build a simple AI ambient medical scribe in Python using AssemblyAI.
In this tutorial, we start with a basic transcription request in under 30 lines of code and incrementally add features that matter in real clinical workflows. You’ll see how to improve medical transcription accuracy with key terms, label speakers by role (doctor vs. patient), generate SOAP notes using LLMs, and apply PII redaction to protect patient privacy.
We’ll also cover how to delete transcription and LLM data after processing so your application meets strict data retention and privacy requirements. By the end, you’ll have a practical foundation for building a production-ready ambient medical scribe.
Timestamps:
00:00 – Intro: Why accuracy and privacy matter for AI medical scribes
00:40 – Building a basic transcription pipeline in Python
01:01 – Speaker identification (doctor vs. patient)
02:18 – Generating SOAP notes with LLMs
02:56 – PII redaction and data privacy
03:40 – Deleting data and retention best practices
04:57 – Final recap
▬▬▬▬▬▬▬▬▬▬▬▬ CONNECT ▬▬▬▬▬▬▬▬▬▬▬▬
🖥️ Website: assemblyai.com
🐦 Twitter: twitter.com/AssemblyAI
👽 Reddit: reddit.com/r/assemblyai
▶️ Subscribe: youtube.com/c/AssemblyAI?sub_confirmation=1
🔥 We're hiring! Check our open roles: assemblyai.com/careers
▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬
