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🗺️ MedMemory Roadmap

This roadmap outlines the planned evolution of MedMemory from a lightweight MVP into a production-grade AI-powered Personal Medical Memory platform.

🚀 Phase 1 — MVP (Current)

Goal: Build a complete end-to-end AI Medical Memory application using lightweight technologies and free-tier infrastructure.

Backend

  • [x] FastAPI REST API
  • [x] JWT Authentication
  • [x] Prisma + PostgreSQL
  • [x] Document Upload APIs
  • [x] OCR Pipeline
  • [x] Medical Information Extraction
  • [x] Timeline Generation API
  • [x] RAG Chat API

AI / RAG

  • [x] PyMuPDF
  • [x] Tesseract OCR
  • [x] LangGraph Workflow
  • [x] Ollama Local LLM
  • [x] Qwen 2.5
  • [x] mxbai-embed-large
  • [x] Qdrant Vector Database

Frontend

  • [x] React Native App
  • [x] Authentication
  • [x] Upload Documents
  • [x] Patient Timeline
  • [x] AI Medical Chat

Deployment

  • [x] OCI Free Tier VM
  • [x] Ollama Server
  • [x] FastAPI Server
  • [x] Neon PostgreSQL
  • [x] Qdrant Cloud

🚀 Phase 2 — Improved Document Intelligence

Goal: Improve extraction quality and support more medical document formats.

Document Processing

  • [ ] Docling
  • [ ] Better OCR pipeline
  • [ ] Multi-page layout understanding
  • [ ] Table extraction
  • [ ] Handwritten prescription support
  • [ ] Signature & stamp detection
  • [ ] Better scanned PDF support

AI Extraction

  • [ ] Advanced prompt engineering
  • [ ] Multi-stage extraction pipeline
  • [ ] Medical terminology normalization
  • [ ] Improved medication extraction
  • [ ] Better diagnosis extraction
  • [ ] Improved laboratory report parsing
  • [ ] Confidence scoring for extracted data

🚀 Phase 3 — Better AI Models

Goal: Improve extraction accuracy and response quality.

Local LLMs

  • [ ] Larger Qwen models
  • [ ] Llama models
  • [ ] Medical domain LLM evaluation
  • [ ] Better embedding models

AI Features

  • [ ] Structured Output Validation
  • [ ] Self-correction pipeline
  • [ ] Hallucination reduction
  • [ ] Citation-based responses
  • [ ] Better medical reasoning
  • [ ] Multi-agent extraction workflow

🚀 Phase 4 — Medical Imaging Support

Goal: Support diagnostic imaging.

Imaging

  • [ ] DICOM support
  • [ ] CT Scans
  • [ ] MRI
  • [ ] PET
  • [ ] Ultrasound
  • [ ] X-Ray

Processing

  • [ ] DICOM metadata extraction
  • [ ] Imaging timeline
  • [ ] AI-assisted scan summaries
  • [ ] Imaging report linking

🚀 Phase 5 — Advanced Patient Timeline

Goal: Build a comprehensive lifelong medical timeline.

Timeline

  • [ ] Disease progression
  • [ ] Medication history
  • [ ] Side effects timeline
  • [ ] Surgery history
  • [ ] Vaccination history
  • [ ] Allergy tracking
  • [ ] Chronic disease monitoring

Analytics

  • [ ] Health trends
  • [ ] Disease recurrence
  • [ ] Medication adherence
  • [ ] Recovery tracking
  • [ ] Health insights dashboard

🚀 Phase 6 — Smarter AI Assistant

Goal: Make the AI a long-term medical assistant.

AI

  • [ ] Follow-up questions
  • [ ] Personalized health summaries
  • [ ] Visit preparation
  • [ ] Medication reminders
  • [ ] Doctor-friendly summaries
  • [ ] Long-context conversations
  • [ ] Memory optimization

🚀 Phase 7 — Production Infrastructure

Goal: Make MedMemory production-ready.

DevOps

  • [ ] Docker Compose
  • [ ] GitHub Actions CI/CD
  • [ ] Multi-stage Docker Images
  • [ ] Production Logging
  • [ ] Monitoring
  • [ ] Metrics
  • [ ] Distributed Tracing

Kubernetes

  • [ ] Kubernetes manifests
  • [ ] Helm Charts
  • [ ] AWS EKS Deployment
  • [ ] Oracle Cloud Kubernetes Deployment
  • [ ] Horizontal Pod Autoscaling
  • [ ] Rolling Updates
  • [ ] Secrets Management

🚀 Phase 8 — Security & Compliance

Goal: Improve security and privacy.

Security

  • [ ] Refresh Tokens
  • [ ] Rate Limiting
  • [ ] Audit Logs
  • [ ] File Encryption
  • [ ] Database Encryption
  • [ ] Role-Based Access Control

Privacy

  • [ ] Data export
  • [ ] Account deletion
  • [ ] Secure backups
  • [ ] Data retention policies

🚀 Phase 9 — Ecosystem

Goal: Expand MedMemory beyond document storage.

Integrations

  • [ ] Wearables
  • [ ] Health APIs
  • [ ] Smartwatch data
  • [ ] Laboratory integrations
  • [ ] Hospital integrations
  • [ ] Cloud storage connectors

🌍 Long-Term Vision

MedMemory aims to become an AI-powered lifelong Personal Medical Memory that:

  • Organizes a patient's complete medical history into a structured timeline.
  • Understands prescriptions, reports, discharge summaries, and medical imaging.
  • Enables secure AI-powered conversations over personal medical history.
  • Assists patients and healthcare professionals with faster and more informed clinical decisions.
  • Runs efficiently on both free-tier and production-grade cloud infrastructure.