Medical Imaging AI Assistant
An AI-powered diagnostic assistant analyzing medical images (X-rays, CT scans, MRIs) with radiologist-level accuracy. Features explainable AI to build clinical trust, HIPAA compliance, and integration with PACS systems. Reduces diagnosis time while maintaining high accuracy.
Project Overview
Technologies Used
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The Challenge
Radiologist shortage caused 3-5 day delays in critical diagnosis, impacting patient outcomes. High burnout rates from repetitive analysis tasks. No second-opinion system for quality assurance. Need to maintain clinical accuracy while accelerating workflow.
Our Solution
We developed deep learning models using convolutional neural networks trained on 2M+ annotated medical images. Implemented explainable AI with attention maps and confidence scores. Built HIPAA-compliant infrastructure with PACS integration. Created workflow tools for radiologist review and validation.
Our Approach
PyTorch
Core framework powering the application architecture and user experience.
FastAPI
Essential technology enabling scalability and performance optimization.
DICOM Processing
Critical infrastructure component for data management and persistence.
AWS HealthLake
Supporting technology enhancing system capabilities and integration.
React
Additional tooling for monitoring, deployment, and operations.
The Results
96.5% diagnostic accuracy validated by radiologists
60% reduction in time to diagnosis
500K+ images analyzed to date
Full HIPAA compliance with audit trails
Explainable AI for clinical trust and validation
Radiologist burnout reduced through workflow optimization
Second-opinion capability improving quality assurance
Key Metrics
Business Impact
Dramatically improved patient outcomes through faster diagnosis while addressing radiologist shortage. Explainable AI built clinician trust and improved adoption rates.
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