Feature and its Use Cases
Overview
Currently, the transcription pipeline generates unstructured text outputs for summary and prescription, which limits scalability and real-world usability.
Problem
- AI responses are plain text
- No structured medical data extraction
- Difficult to extend for future features (EHR, analytics, diagnosis)
Proposed Solution
Introduce a structured AI response system using a new MedicalInsights model.
Features
- JSON-based AI output (summary, symptoms, medicines)
- New domain model:
MedicalInsights
- Improved Gemini service for structured parsing
- Controller updated to handle structured data
- UI ready for rendering structured insights
Architecture
Audio → Deepgram → Transcript
→ Gemini AI (JSON output)
→ MedicalInsights Model
→ Controller → UI
Impact
- Makes the app closer to real-world healthcare use
- Enables future extensions (diagnosis, analytics)
- Improves maintainability and scalability
Tasks
Additional Context
No response
Code of Conduct
Feature and its Use Cases
Overview
Currently, the transcription pipeline generates unstructured text outputs for summary and prescription, which limits scalability and real-world usability.
Problem
Proposed Solution
Introduce a structured AI response system using a new
MedicalInsightsmodel.Features
MedicalInsightsArchitecture
Audio → Deepgram → Transcript
→ Gemini AI (JSON output)
→ MedicalInsights Model
→ Controller → UI
Impact
Tasks
Additional Context
No response
Code of Conduct