2000 Hours Real Doctor-Patient Conversations
Transform your AI training with 2000 hours of human transcribed and tagged live healthcare conversations. Available in English, this dataset includes classification tags for medical domains.
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Leverage this dataset to:
- Enhance your AI training: With 2000 hours of live doctor-patient conversations, recorded in-office or via telehealth, this dataset provides a vast amount of real-world dialogue data to train and improve your Conversational AI models. Each conversation is human-transcribed, verified by a medical expert, and tagged with classification labels, indicating the medical domain covered in the conversation. This allows for tailored and accurate training of AI models for the healthcare industry.
- Improve your clinical decision support: By utilizing this dataset, you can refine your decision-making tools by analyzing authentic doctor-patient interactions. With this data, you can train your AI models to understand and interpret medical conversations, providing valuable insights to assist in clinical decision-making.
- Medical Virtual Assistants: Utilize the dataset to train AI models for developing chatbots and virtual assistants capable of engaging in natural language conversations about healthcare topics. These AI assistants can provide personalized health advice, answer medical questions, and assist with appointment scheduling and medication reminders.
Uses Cases:
- Medical Transcription and Speech-to-Text Conversion
- Health Literacy Improvement
- Healthcare Information Retrieval and Recommendation
Technical Specifications
- Type: Audio + Transcription
- Language: English
- Quantity: 2000
- Unit: hours
- Domain: Healthcare
- Data Type: Live Audio
- File Format: WAV & JSON
- Sample Rate: 16 kHz
- Bit Rate: 16 bit
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License Information
This dataset is covered by our standard Data license agreement. The license agreement is perpetual and allows for the commercialization of all models built on the data.
