Medical scribe

import assemblyai as aai
import requests

# ── Config ────────────────────────────────────────────────────
api_key = "YOUR_API_KEY"
aai.settings.api_key = api_key
headers = {"authorization": api_key}

audio_url = "https://assembly.ai/wildfires.mp3"  # Replace with your clinical audio

# ── Step 1: Transcribe with Medical Mode + speaker labels + entity detection ──
config = aai.TranscriptionConfig(
    domain="medical-v1",
    speaker_labels=True,
    entity_detection=True,
)

transcript = aai.Transcriber().transcribe(audio_url, config)

if transcript.error:
    raise RuntimeError(f"Transcription failed: {transcript.error}")

# ── Step 2: Identify speakers by role ──
understanding_response = requests.post(
    "https://llm-gateway.assemblyai.com/v1/understanding",
    headers=headers,
    json={
        "transcript_id": transcript.id,
        "speech_understanding": {
            "request": {
                "speaker_identification": {
                    "speaker_type": "role",
                    "known_values": ["Provider", "Patient"],
                }
            }
        },
    },
)
understanding_response.raise_for_status()
identified = understanding_response.json()

# ── Step 3: Extract detected entities ──
entities = transcript.entities or []
entity_summary = "\n".join(
    f"- {e.entity_type}: {e.text}" for e in entities
)

# ── Step 4: Format identified transcript ──
speaker_transcript = "\n".join(
    f"{u['speaker']}: {u['text']}" for u in identified["utterances"]
)

# ── Step 5: Generate SOAP note via LLM Gateway ──
llm_response = requests.post(
    "https://llm-gateway.assemblyai.com/v1/chat/completions",
    headers=headers,
    json={
        "model": "claude-sonnet-4-6",
        "messages": [
            {
                "role": "user",
                "content": (
                    "You are a medical scribe. Given the clinical encounter transcript and "
                    "detected entities below, generate a structured SOAP note.\n\n"
                    "Format the note with these sections:\n"
                    "- **Subjective**: Patient's reported symptoms and history\n"
                    "- **Objective**: Clinical observations and measurements\n"
                    "- **Assessment**: Diagnosis or clinical impression\n"
                    "- **Plan**: Treatment plan, prescriptions, and follow-up\n\n"
                    f"Detected entities:\n{entity_summary}\n\n"
                    f"Transcript:\n{speaker_transcript}"
                ),
            }
        ],
        "max_tokens": 2000,
    },
)
llm_response.raise_for_status()

print("=== SOAP Note ===\n")
print(llm_response.json()["choices"][0]["message"]["content"])

const baseUrl = "https://api.assemblyai.com";
const headers = {
  authorization: "YOUR_API_KEY",
  "Content-Type": "application/json",
};

const audioUrl = "https://assembly.ai/wildfires.mp3"; // Replace with your clinical audio

// Step 1: Transcribe with Medical Mode + speaker labels + entity detection
let res = await fetch(`${baseUrl}/v2/transcript`, {
  method: "POST",
  headers,
  body: JSON.stringify({
    audio_url: audioUrl,
    domain: "medical-v1",
    speaker_labels: true,
    entity_detection: true,
  }),
});
if (!res.ok) throw new Error(`Error: ${res.status}`);
const { id: transcriptId } = await res.json();

let result;
while (true) {
  res = await fetch(`${baseUrl}/v2/transcript/${transcriptId}`, { headers });
  result = await res.json();
  if (result.status === "completed") break;
  if (result.status === "error")
    throw new Error(`Transcription failed: ${result.error}`);
  await new Promise((r) => setTimeout(r, 3000));
}

// Step 2: Identify speakers by role
const understandingRes = await fetch(
  "https://llm-gateway.assemblyai.com/v1/understanding",
  {
    method: "POST",
    headers,
    body: JSON.stringify({
      transcript_id: transcriptId,
      speech_understanding: {
        request: {
          speaker_identification: {
            speaker_type: "role",
            known_values: ["Provider", "Patient"],
          },
        },
      },
    }),
  }
);
if (!understandingRes.ok) throw new Error(`Error: ${understandingRes.status}`);
const identified = await understandingRes.json();

// Step 3: Extract detected entities
const entities = result.entities || [];
const entitySummary = entities
  .map((e) => `- ${e.entity_type}: ${e.text}`)
  .join("\n");

// Step 4: Format identified transcript
const speakerTranscript = identified.utterances
  .map((u) => `${u.speaker}: ${u.text}`)
  .join("\n");

// Step 5: Generate SOAP note via LLM Gateway
res = await fetch("https://llm-gateway.assemblyai.com/v1/chat/completions", {
  method: "POST",
  headers,
  body: JSON.stringify({
    model: "claude-sonnet-4-6",
    messages: [
      {
        role: "user",
        content:
          "You are a medical scribe. Given the clinical encounter transcript and " +
          "detected entities below, generate a structured SOAP note.\n\n" +
          "Format the note with these sections:\n" +
          "- **Subjective**: Patient's reported symptoms and history\n" +
          "- **Objective**: Clinical observations and measurements\n" +
          "- **Assessment**: Diagnosis or clinical impression\n" +
          "- **Plan**: Treatment plan, prescriptions, and follow-up\n\n" +
          `Detected entities:\n${entitySummary}\n\n` +
          `Transcript:\n${speakerTranscript}`,
      },
    ],
    max_tokens: 2000,
  }),
});
if (!res.ok) throw new Error(`Error: ${res.status}`);
const llmResult = await res.json();

console.log("=== SOAP Note ===\n");
console.log(llmResult.choices[0].message.content);

import { AssemblyAI } from "assemblyai";

// ── Config ────────────────────────────────────────────────────
const apiKey = "YOUR_API_KEY";
const client = new AssemblyAI({ apiKey });
const headers = {
  authorization: apiKey,
  "Content-Type": "application/json",
};

const audioUrl = "https://assembly.ai/wildfires.mp3"; // Replace with your clinical audio

// Step 1: Transcribe with Medical Mode + speaker labels + entity detection
const transcript = await client.transcripts.transcribe({
  audio: audioUrl,
  domain: "medical-v1",
  speaker_labels: true,
  entity_detection: true,
});

if (transcript.error) {
  throw new Error(`Transcription failed: ${transcript.error}`);
}

// Step 2: Identify speakers by role
const understandingRes = await fetch(
  "https://llm-gateway.assemblyai.com/v1/understanding",
  {
    method: "POST",
    headers,
    body: JSON.stringify({
      transcript_id: transcript.id,
      speech_understanding: {
        request: {
          speaker_identification: {
            speaker_type: "role",
            known_values: ["Provider", "Patient"],
          },
        },
      },
    }),
  }
);
if (!understandingRes.ok) throw new Error(`Error: ${understandingRes.status}`);
const identified = await understandingRes.json();

// Step 3: Extract detected entities
const entities = transcript.entities || [];
const entitySummary = entities
  .map((e) => `- ${e.entity_type}: ${e.text}`)
  .join("\n");

// Step 4: Format identified transcript
const speakerTranscript = identified.utterances
  .map((u) => `${u.speaker}: ${u.text}`)
  .join("\n");

// Step 5: Generate SOAP note via LLM Gateway
const llmRes = await fetch(
  "https://llm-gateway.assemblyai.com/v1/chat/completions",
  {
    method: "POST",
    headers,
    body: JSON.stringify({
      model: "claude-sonnet-4-6",
      messages: [
        {
          role: "user",
          content:
            "You are a medical scribe. Given the clinical encounter transcript and " +
            "detected entities below, generate a structured SOAP note.\n\n" +
            "Format the note with these sections:\n" +
            "- **Subjective**: Patient's reported symptoms and history\n" +
            "- **Objective**: Clinical observations and measurements\n" +
            "- **Assessment**: Diagnosis or clinical impression\n" +
            "- **Plan**: Treatment plan, prescriptions, and follow-up\n\n" +
            `Detected entities:\n${entitySummary}\n\n` +
            `Transcript:\n${speakerTranscript}`,
        },
      ],
      max_tokens: 2000,
    }),
  }
);
if (!llmRes.ok) throw new Error(`Error: ${llmRes.status}`);
const llmResult = await llmRes.json();

console.log("=== SOAP Note ===\n");
console.log(llmResult.choices[0].message.content);