AI-Driven Clinical Support Platform for Breast Cancer Diagnosis

Key Goals

• Enable early breast cancer detection using AI image analysis
• Extract insights from unstructured pathology reports
• Support clinical decisions via Retrieval-Augmented Generation (RAG)
• Prevent LLM training on patient data
• Ensure HIPAA/GDPR compliance and full auditability
• Lower diagnostic turnaround time and cost

Example: A rural hospital auto-summarizes mammograms, helping general physicians make timely referrals.

Architecture Overview

Flow:
User → UI → Middleware → Preprocessing → Image/NLP Engine → RAG Retriever → LLM → Formatter → UI

1. Upload: Pathologist uploads image/report via UI
2. Middleware: Authenticates, logs request, assigns Task ID
3. Preprocessing:
   • Image: OCR → De-ID
   • Text: Direct De-ID
   • Metadata extraction
4. Image Analysis: CNNs (ResNet, Swin) detect ROIs; overlays via Grad-CAM
5. NLP Parsing: BioBERT extracts facts like ER/PR/HER2, tumor size, staging
6. Context Compilation: Middleware prepares context
7. RAG Query: Embeds query/context, searches FAISS/Pinecone for guidelines
8. LLM Output: GPT-4/BioGPT returns insights with citations, confidence scores
9. Format & Deliver: Final response sent to UI; logged for audit

Core Features

• Image Diagnostics: Mammogram classification with heatmaps
• NLP Reports: Structured fact extraction from reports
• RAG Engine: Fetches clinical context from NCCN, WHO, PubMed
• Middleware: REST APIs, RBAC, Redis caching, OAuth2 security
• Audit: Kibana dashboard, immutable logs, alerts for anomalies

Deployment & Infra

• Kubernetes, Dockerized services
• Observability: Prometheus, Grafana, ELK Stack
• Storage: AWS S3, Azure Blob
• LLM APIs: GPT-4, BioGPT (no PHI exposure)
• Security: TLS 1.2+, AES-256 encryption, de-ID on ingestion

UI Highlights

• Uploads (PDF, image)
• AI chat for clinical queries
• Visual summaries with facts & insights
• Admin portal for usage logs

Results & Impact

• Diagnosis time cut by 60%
• Report review cost lowered by 40%
• 88% user confidence in pilot rollout
• FDA AI/ML guidance aligned

Execution Roadmap

1. Prototype: Train models on public datasets, test RAG
2. Build: Integrate pipelines and secure endpoints
3. Audit: Apply compliance standards
4. Pilot: Deploy in one hospital, gather feedback

Start Now:
Form advisory group → Define test cases → Deploy MVP