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| # SanjeevaniAI — Implementation Plan | |
| ## Phase Overview | |
| The construction of **SanjeevaniAI** follows a disciplined 15-phase engineering roadmap designed to deliver a robust, modular, testable, and presentation-ready healthcare AI platform. | |
| --- | |
| ## Roadmap | |
| ### Phase 1: Architecture & Project Audit (Completed) | |
| - [x] Complete directory scan and asset inventory | |
| - [x] Model verification for `models/bc5cdr-ner` (RoBERTa-large BC5CDR Token Classification) | |
| - [x] Create `docs/PROJECT_AUDIT.md` and `docs/IMPLEMENTATION_PLAN.md` | |
| - [x] Establish architecture diagrams and technical specifications | |
| ### Phase 2: Environment Configuration & Dependencies | |
| - [ ] Update `requirements.txt` with FastAPI, Pydantic v2, SQLAlchemy 2.0, Alembic, python-jose, passlib, pypdf, httpx, uvicorn, redis, etc. | |
| - [ ] Create `.env.example` and default `.env` configuration | |
| - [ ] Install missing backend packages into `D:\SanjeevaniAI\.venv` | |
| ### Phase 3: Backend Foundation & Modular Architecture | |
| - [ ] Scaffold `backend/` directory structure: | |
| - `app/main.py`: Application factory with lifespan event handling, CORS, rate limiting, and exception handlers | |
| - `app/core/`: Settings (`config.py`), security (`security.py`), logging (`logger.py`), exceptions (`exceptions.py`) | |
| - `app/api/v1/`: API Routers for auth, users, ner, documents, chat, history, profile, and admin | |
| - `app/models/`: SQLAlchemy ORM models (users, profiles, documents, entities, conversations, audit_logs) | |
| - `app/schemas/`: Pydantic validation schemas | |
| - `app/repositories/`: Data access layer | |
| - `app/services/`: Business logic layer | |
| - `app/ml/`: Model manager & local NER adapter | |
| - `app/middleware/`: Request ID, audit logging, security headers | |
| ### Phase 4: Local Biomedical NER Model Integration | |
| - [ ] Implement `app/ml/ner/base.py` (Abstract NER interface) | |
| - [ ] Implement `app/ml/ner/bc5cdr.py` (Local RoBERTa-large BC5CDR inference engine) | |
| - [ ] Implement `app/ml/ner/service.py` & `app/ml/manager.py` (Lifecycle, warmup, error isolation, batch inference) | |
| - [ ] Expose `POST /api/v1/ner/analyze` with confidence scores, offsets, entity types (`CHEMICAL`, `DISEASE`), and model metadata | |
| - [ ] Unit & integration tests for NER pipeline | |
| ### Phase 5: Database Schema & Migrations | |
| - [ ] Configure SQLAlchemy async engine (supporting PostgreSQL with SQLite dev fallback) | |
| - [ ] Define normalized models: `User`, `Role`, `PatientProfile`, `MedicalDocument`, `DocumentAnalysis`, `MedicalEntity`, `AIConversation`, `AIMessage`, `AuditLog` | |
| - [ ] Setup Alembic migration environment (`alembic/`) | |
| - [ ] Generate and apply baseline database migration | |
| ### Phase 6: Authentication & Authorization (RBAC) | |
| - [ ] Secure JWT authentication (Access & Refresh tokens) | |
| - [ ] Password hashing via Argon2 / Bcrypt | |
| - [ ] RBAC for `USER`, `PATIENT`, `DOCTOR`, `ADMIN` | |
| - [ ] Endpoints: `POST /api/v1/auth/register`, `POST /api/v1/auth/login`, `POST /api/v1/auth/refresh`, `GET /api/v1/auth/me` | |
| ### Phase 7: Medical Document Analysis Pipeline | |
| - [ ] Secure multi-format text extraction (`PDF`, `TXT`, `DOCX`) | |
| - [ ] Filename sanitization, SHA256 integrity hashing, size limits | |
| - [ ] Pipeline: Upload -> Text Extraction -> Normalization -> BC5CDR NER -> Clinical Summarization -> Structured Storage | |
| - [ ] Endpoints: `POST /api/v1/documents/upload`, `GET /api/v1/documents`, `GET /api/v1/documents/{id}`, `DELETE /api/v1/documents/{id}` | |
| ### Phase 8: AI Medical Assistant & Multi-Provider Abstraction | |
| - [ ] Implement `BaseLLMProvider`, `GeminiProvider` (Google GenAI API), and `MockLLMProvider` (offline fallback) | |
| - [ ] Strict clinical safety guardrails (extracted facts vs. AI considerations, emergency disclaimers, no speculative diagnoses) | |
| - [ ] Endpoints: `POST /api/v1/chat/completions`, `GET /api/v1/chat/conversations`, `DELETE /api/v1/chat/conversations/{id}` | |
| ### Phase 9: Patient Profile, Medical History & Audit Logging | |
| - [ ] Patient profile management (`GET`/`PUT /api/v1/profile`) | |
| - [ ] Chronological medical timeline (`GET /api/v1/history`) | |
| - [ ] Structured security audit logger for sensitive events (`LOGIN`, `DOCUMENT_UPLOAD`, `AI_ANALYSIS`, `ADMIN_ACTION`) | |
| ### Phase 10: Frontend Foundation (Next.js 14+ / React / Tailwind) | |
| - [ ] Initialize Next.js TypeScript project in `frontend/` | |
| - [ ] Configure Tailwind CSS, Lucide React icons, and accessible component library | |
| - [ ] Create layout architecture: Sidebar navigation, Header, Breadcrumbs, Dark/Light theme, Clinical Disclaimer Banner | |
| - [ ] Setup API Client with Axios/TanStack Query and JWT authentication context | |
| ### Phase 11: Frontend Pages & Features | |
| - [ ] **Landing Page (`/`)**: Hero, product capabilities, architecture overview, medical safety disclaimer | |
| - [ ] **Auth Pages (`/login`, `/register`)**: Accessible forms with Zod validation | |
| - [ ] **Dashboard (`/dashboard`)**: Summary metric cards, recent document analyses, detected conditions, activity timeline | |
| - [ ] **Biomedical NER Visualizer (`/ner`)**: Dedicated mentor demo page with live entity highlighting (`CHEMICAL` / `DISEASE`), confidence metrics, and execution latency | |
| - [ ] **Medical Document Manager (`/reports`, `/reports/[id]`)**: Drag-and-drop file upload, progress bar, clinical entity viewer, summary export | |
| - [ ] **AI Assistant Interface (`/assistant`)**: Conversational UI, structured medical guidance, chat history, emergency alerts | |
| - [ ] **Patient Profile & Timeline (`/profile`, `/history`)**: Editable vitals/allergies/medications and chronological action history | |
| - [ ] **Admin & System Health (`/admin`)**: User statistics, model usage metrics, system logs, audit trails | |
| - [ ] **Mentor Demonstration Mode (`/demo`)**: Synthetic patient flow for 10-minute presentation | |
| ### Phase 12: Seed Data & Testing Suites | |
| - [ ] Synthetic seed generator: `scripts/seed_demo_data.py` (No real patient data) | |
| - [ ] Backend test suite: `pytest` covering auth, NER, document analysis, RBAC, error handlers | |
| - [ ] Frontend validation: Lint, TypeScript build verification | |
| ### Phase 13: Containerization & Docker Setup | |
| - [ ] `backend/Dockerfile` and `frontend/Dockerfile` | |
| - [ ] `docker-compose.yml` (Backend, Frontend, PostgreSQL, Redis) | |
| - [ ] Model volume mounting configuration | |
| ### Phase 14: CI/CD & Security Auditing | |
| - [ ] GitHub Actions workflows: `.github/workflows/test.yml`, `.github/workflows/lint.yml` | |
| - [ ] Security hardening: Rate limiting, CORS policies, XSS/CSRF headers, path traversal protection | |
| ### Phase 15: Documentation & Presentation Package | |
| - [ ] Root `README.md` | |
| - [ ] `docs/ARCHITECTURE.md` (with Mermaid diagrams) | |
| - [ ] `docs/API.md` (OpenAPI specification & curl examples) | |
| - [ ] `docs/ML_MODELS.md` (BC5CDR NER specs & performance metrics) | |
| - [ ] `docs/MENTOR_DEMO.md` (Step-by-step 10-15 minute demonstration script) | |