Medical Conversation Analysis Tool

Automated Transcription, Classification, and Summarization for Healthcare Professionals

This project provides a comprehensive suite of tools for processing and analyzing medical conversations between doctors and patients. It leverages cutting-edge natural language processing techniques to transcribe audio, classify speaker roles, and generate concise summaries.

Key Features

1. Audio Transcription: Utilizes OpenAI's Whisper model to convert audio recordings of medical conversations into text.
2. Speaker Classification: Implements both AI-powered and rule-based approaches to distinguish between doctor and patient speech.
3. Conversation Summarization: Employs advanced transformer models to create concise summaries of medical dialogues.
4. Medical Named Entity Recognition: Integrates with Hugging Face's Medical-NER model for identifying medical terms and concepts.

Use Cases

• Streamline medical documentation processes
• Enhance patient record accuracy and completeness
• Support medical research and analysis
• Improve healthcare provider training and education

Getting Started

1. Clone the repository
2. Install required dependencies
3. Set up API keys for OpenAI services
4. Run the main script to process your medical conversations

Technologies Used

• Python
• OpenAI Whisper
• Transformers library (Hugging Face)
• NLTK
• spaCy

License

This project is licensed under the MIT License. See the LICENSE file for details.

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Enhance your medical practice with our state-of-the-art conversation analysis tool. Improve patient care, streamline documentation, and gain valuable insights from doctor-patient interactions.