Mediphany AI
Challenge
Healthcare practitioners faced a challenge: spending too much time on admin tasks instead of patient care. A solution was needed to turn speech from video recordings into detailed reports, map transcriptions to templates, and ensure flexibility for different cases.
Healthcare practitioners faced a challenge: spending too much time on admin tasks instead of patient care. A solution was needed to turn speech from video recordings into detailed reports, map transcriptions to templates, and ensure flexibility for different cases.
Solution
Scopic created an AI-driven solution for automating structured report generation from practitioners’ spoken input. The AI transcribes speech from video recordings, selects templates, and maps content into report fields using Speech-to-Text and LLM technologies.
Scopic created an AI-driven solution for automating structured report generation from practitioners’ spoken input. The AI transcribes speech from video recordings, selects templates, and maps content into report fields using Speech-to-Text and LLM technologies.
Results
Radiologists produce high-quality, structured reports more efficiently without compromising accuracy.
- Streamlined Workflow: Enables radiologists to select cases, initiate AI-assisted recordings, and review key sections before approving reports.
- Enhanced Accuracy: Deepgram’s Nova-2-Medical Model ensures precise MRI transcription for structured reports.
- Structured Formats: LangChain with GPT-4 maps transcriptions to consistent formats (e.g., knee MRI, brain MRI).
- Continuous Improvement: Chroma integration uses a vector store for context, ensuring more accurate reports over time.
Technologies and Tools
- Speech-to-Text (STT): For converting speech into text accurately.
- Large Language Model (LLM): Powers advanced transcription and content mapping.
- Python: Used for developing the AI pipeline and integration.
- OpenAI API: Powers GPT-4 for transcription and mapping to structured formats.
- LangChain, LangServe, and LangSmith: For managing AI workflows, transcription mapping, and model deployment.
- Deepgram’s Nova-2-Medical Model: Optimized speech-to-text model for accurate medical transcription.
Radiologists produce high-quality, structured reports more efficiently without compromising accuracy.
- Streamlined Workflow: Enables radiologists to select cases, initiate AI-assisted recordings, and review key sections before approving reports.
- Enhanced Accuracy: Deepgram’s Nova-2-Medical Model ensures precise MRI transcription for structured reports.
- Structured Formats: LangChain with GPT-4 maps transcriptions to consistent formats (e.g., knee MRI, brain MRI).
- Continuous Improvement: Chroma integration uses a vector store for context, ensuring more accurate reports over time.
Technologies and Tools
- Speech-to-Text (STT): For converting speech into text accurately.
- Large Language Model (LLM): Powers advanced transcription and content mapping.
- Python: Used for developing the AI pipeline and integration.
- OpenAI API: Powers GPT-4 for transcription and mapping to structured formats.
- LangChain, LangServe, and LangSmith: For managing AI workflows, transcription mapping, and model deployment.
- Deepgram’s Nova-2-Medical Model: Optimized speech-to-text model for accurate medical transcription.