- README.md: Updated Tech Stack (MedGemma via HF Endpoint, not Gemma 3 via Google AI Studio),
added 50-case MedQA validation results, updated Quick Start for HF token, added
deploy guide to doc index and project structure, added analyze/check_progress to tree
- docs/architecture.md: Updated LLM label, model config, API dependencies, configuration
table defaults, system prompt section (TGI supports system role natively), known limitations
- docs/test_results.md: Added 50-case MedQA results with question-type breakdown, updated header
- docs/writeup_draft.md: Added MedQA 50-case results to performance table, updated validation
section from 'in progress' to actual numbers, updated latency note
- DEVELOPMENT_LOG.md: Fixed environment config table defaults, added Phase 11 (MedGemma HF
Endpoint deployment) and Phase 12 (50-case MedQA validation with full results/analysis)
- TODO.md: Complete rewrite - marked validation as done, updated model info from gemma-3 to
MedGemma, updated…
Add conflict detection step to CDS pipeline
bshepp
e684a6c
- Add ConflictType enum, ClinicalConflict, ConflictDetectionResult models to schemas
- Add conflicts field to CDSReport and conflict_detection to AgentState
- Create conflict_detection.py tool (compares guidelines vs patient data)
- Wire as Step 5 in orchestrator between guideline retrieval and synthesis
- Update synthesis to accept and format conflict data in prompt
- Add fallback to copy detected conflicts into report if LLM misses them
- Add prominent Conflicts & Gaps section to frontend CDSReport.tsx
with severity-coded cards, guideline vs patient data comparison,
and suggested resolutions
Initial commit: CDS Agent - Clinical Decision Support System
bshepp
f2c113d
- 5-step agentic pipeline: parse reason drug check RAG guidelines synthesize
- Backend: FastAPI + Python 3.10, Gemma 3 27B IT via Google AI Studio
- Frontend: Next.js 14 + React 18 + TypeScript + Tailwind CSS
- RAG: ChromaDB with 62 clinical guidelines across 14 specialties
- Drug APIs: OpenFDA + RxNorm/NLM
- Tests: E2E pipeline, 30 RAG quality queries (100% pass), 22 clinical scenarios
- Full documentation: architecture, test results, development log, writeup
