Build reliable ambient AI scribes for clinical environments

Yes—when enabled. Set redact_pii: true to automatically replace PHI in the transcript, and optionally use redact_pii_policies. You can also mute PHI in audio with redact_pii_audio: true.

AssemblyAI segments clinical audio into speaker‑labeled turns. Enable speaker_labels (optionally set speakers_expected) and use role-based Speaker Identification (e.g., Doctor/Patient). In streaming, format_turns returns structured, speaker-aware output. The platform supports multi-speaker clinical settings (consults, rounds) and improves separation in noisy/overlapping speech.

AssemblyAI captures medical jargon using its Slam-1 model built for clinical transcription, plus context via a Keyterms prompt (patient history, specialty, visit context). For live use, Universal-Streaming is optimized for medical contexts. The platform handles pharma names and acronyms and reduces missed medical entities by up to 66%.

AssemblyAI secures patient data with AES‑128/256 encryption at rest and TLS 1.2+ in transit. It offers HIPAA‑compliant workflows with Business Associate Agreements (BAA) and optional EU data residency, and provides PII redaction to automatically remove sensitive information.

Ambient AI refers to AI that operates in the background during real‑world interactions, turning conversations into structured data and automation without manual effort. In practice, systems transcribe live speech with low latency and extract insights to automate documentation and agent assist in domains like healthcare and contact centers.

Healthcare teams use conversational/voice AI for ambient clinical documentation (real-time transcription, speaker diarization, and LLM‑generated SOAP notes), telehealth and ED encounters with low‑latency streaming, and HIPAA‑compliant PII redaction (text and audio). Beyond documentation, systems also support intelligent triage and patient education.