Medical Image Analysis Engine
AI-powered diagnostic pipeline for rapid medical image analysis
An AI-native processing pipeline utilizing custom computer vision models and RAG data layers to extract rapid, structured diagnostic insights.
An AI-native processing pipeline utilizing custom computer vision models and RAG data layers to extract rapid, structured diagnostic insights.
Project Overview
We built a HIPAA-compliant medical image analysis platform that uses custom computer vision models to assist radiologists in detecting anomalies from X-ray, MRI, and CT scan imagery. The platform processes 10,000+ images daily with 95% detection precision, reducing radiologist review time by 40%. The system integrates with existing PACS and EHR workflows through FHIR-compliant APIs.
The Challenge
The healthcare provider was facing radiologist burnout with image review queues exceeding 48 hours. Existing AI solutions had high false-positive rates that eroded clinician trust. The solution needed HIPAA compliance, seamless EHR integration, and sub-5-second processing per image — all while maintaining patient data privacy end-to-end.
Our Solution
We developed a custom PyTorch-based computer vision pipeline with ensemble models for anomaly detection. A RAG layer grounds AI findings in the patient's medical history and provides explainable reasoning for each detection. The entire pipeline operates on HIPAA-compliant AWS infrastructure with encrypted storage, audit logging, and strict access controls.
Business Impact
The platform reduced average image review time from 15 minutes to 9 minutes per study. Detection precision reached 95% with a false-positive rate under 3%. The system processes over 10,000 images daily across three hospital networks, and radiologist satisfaction scores improved by 60%.
Visual Highlights
Key Features
Technical capabilities that made this project successful
Ensemble CV Models
Multiple PyTorch models working in ensemble for robust anomaly detection across imaging modalities.
Explainable AI
Grad-CAM heatmaps and natural language explanations for every detection to build clinician trust.
RAG Grounding Layer
Retrieval-augmented generation that grounds findings in the patient's medical history and clinical context.
FHIR Integration
Seamless integration with Epic and Cerner EHR systems through FHIR R4 compliant APIs.
End-to-End Encryption
Patient data encrypted at rest with AES-256 and in transit with TLS 1.3. Full audit trail for every access.
Automated Triage
Priority scoring system that flags critical findings for immediate radiologist review, reducing time-to-diagnosis.
Technology Stack
Modern toolchain selected for this specific use case
AI/ML
- PyTorch
- TorchVision
- MONAI
- ONNX Runtime
- Hugging Face
Backend
- Python
- FastAPI
- Node.js
- PostgreSQL
- Redis
Integration
- FHIR R4
- HL7 v2
- DICOM
- Epic APIs
- Cerner APIs
Infrastructure
- AWS HealthLake
- Kubernetes
- Terraform
- Vault
- Wazuh
Project Timeline
Delivered in phased increments with continuous stakeholder validation
Data Pipeline
DICOM ingestion pipeline with de-identification, format normalization, and secure storage.
Model Development
Custom PyTorch model training on 500K+ annotated medical images with iterative clinician validation.
RAG & Explainability
RAG layer integration with clinical knowledge base and Grad-CAM explainability interface.
EHR Integration
FHIR API integration, PACS connectivity, and pilot deployment across three hospital networks.
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