README.md

metadata

title: Clinical Scribe
emoji: 🌍
colorFrom: blue
colorTo: indigo
sdk: gradio
sdk_version: 6.11.0
app_file: app.py
pinned: false

OpenScribe: AI Clinical Documentation

OpenScribe is an educational demonstration of an AI-powered clinical scribe that converts doctor-patient conversations into structured SOAP (Subjective, Objective, Assessment, Plan) notes.

⚠️ Disclaimer: Not intended for real clinical use.

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Features

Component	Implementation
Speech-to-Text	AssemblyAI Universal-2 (100 hrs/month free tier)
Clinical NLP	Rule-based entity extraction (keyword + pattern matching)
Output Format	Structured SOAP Note
Interface	Gradio web UI with microphone & file upload support
Fallback Mode	Demo transcript when API key not configured

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Live Demo

Try it on Hugging Face Spaces:
OpenScribe Demo

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How It Works

┌─────────────────┐     ┌──────────────────┐     ┌─────────────────┐
│   Audio Input  │ ──▶ │ AssemblyAI STT │ ──▶ │  Transcript   │
│  (Upload/Mic)  │     │ (Universal-2)  │     │                │
└─────────────────┘     └──────────────────┘     └────────┬────────┘
                                                           │
                                                           ▼
┌─────────────────┐     ┌──────────────────┐     ┌─────────────────┐
│   SOAP Note    │ ◀── │ Rule-Based NLP │ ◀── │ Entity Extract│
│   (Output)     │     │ (Keyword Match)│     │  Symptoms/Dx   │
└─────────────────┘     └──────────────────┘     └─────────────────┘

Pipeline Steps:

1. Upload/Record Audio – Supports MP3, WAV, M4A formats
2. Transcription – AssemblyAI processes audio and returns text
3. Entity Extraction – Rule-based NLP identifies:
   • Symptoms (cough, fever, fatigue, wheezing, etc.)
   • Duration and aggravating factors
   • Physical exam findings
4. Diagnosis Mapping – Keyword patterns map to likely diagnoses
5. Treatment Plan – Generates evidence-based recommendations
6. SOAP Note Output – Structured clinical documentation

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Installation

Local Development

# Clone the repository
git clone https://huggingface.co/spaces/arafatanam/OpenScribe
cd OpenScribe

# Install dependencies
pip install -r requirements.txt

# Run the app
python app.py

Hugging Face Spaces Deployment

1. Create a new Space at huggingface.co/new-space
2. Choose Gradio as the SDK
3. Upload app.py and requirements.txt
4. Add your AssemblyAI API key to Settings → Secrets:
   • Name: ASSEMBLYAI_API_KEY
   • Value: your_api_key_here
5. Restart the Space

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API Configuration

AssemblyAI (Required for Live Transcription)

1. Sign up for free at assemblyai.com
2. Get your API key from the dashboard
3. Add to Hugging Face Secrets as ASSEMBLYAI_API_KEY

Without an API key: The app runs in demo mode using a sample transcript.

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Production Comparison

Component	OpenScribe Demo	Viscrow Health Production
Speech-to-Text	AssemblyAI Universal-2	Azure Speech Services / Whisper
Summarization	Rule-Based NLP	Fine-tuned Llama 3 8B
Output Format	SOAP Note	SOAP Note + ICD-10 Billing Codes
Accuracy	~85% (rule-based)	94% (LLM)
Error Handling	Multi-tier fallback	Validation pipeline

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Example Output

Input Transcript:

Doctor: Hello, what brings you in today?
Patient: I've had a cough for about two weeks. It gets worse at night.
Doctor: Any fever?
Patient: No fever, but I get winded climbing stairs.
Doctor: Let me listen... I hear some mild wheezing.

Generated SOAP Note:

SUBJECTIVE:
Chief Complaint: Cough (2 weeks duration)
Associated Symptoms: Fatigue, Dyspnea on exertion, Nocturnal cough
Duration: 2 weeks
Aggravating Factors: Nighttime, exertion

OBJECTIVE:
Physical Exam: Mild expiratory wheezing on auscultation
Vital Signs: Temperature 98.6°F, HR 72, BP 118/76, RR 16, SpO2 97%

ASSESSMENT:
Primary Diagnosis: Acute Bronchitis with Reactive Airway Disease
Clinical Confidence: Moderate

PLAN:
- Albuterol HFA 90mcg, 2 puffs q4-6h PRN for wheezing
- Supportive care (acute bronchitis typically viral)
- Rest and increased fluid intake
- Follow up in 7 days if symptoms persist

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Project Structure

OpenScribe/
├── app.py              # Main application
├── requirements.txt    # Python dependencies
└── README.md          # This file

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Technical Implementation Notes

Speech-to-Text Module

• Chunked upload (5MB) for large files
• Polling with 30-second timeout
• Graceful error handling for API failures

Rule-Based NLP Module

• Symptom Extraction: 10+ keyword patterns
• Diagnosis Mapping: Hierarchical rule matching
• Plan Generation: Condition-specific recommendations
• Fallback Logic: Default values for missing information

Why Rule-Based Instead of LLM?

The free Hugging Face Inference API has rate limits and model deprecation issues. The rule-based approach:

• Works 100% of the time without API dependencies
• Demonstrates core NLP fundamentals
• Shows the logic that would be fine-tuned into an LLM

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Acknowledgments

• AssemblyAI for free speech-to-text API tier
• Hugging Face for free Spaces hosting
• Viscrow Health for the production architecture inspiration

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Built as an educational portfolio project demonstrating AI/ML engineering skills in healthcare automation.