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Healthcare AI

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.

Client:Regional Hospital Network (15 hospitals)
Timeline:10 months

Project Overview

Timeline
10 months
Team
4 ML engineers, 1 medical consultant
Industry
Healthcare AI

Technologies Used

PyTorchFastAPIDICOM ProcessingAWS HealthLakeReactPostgreSQL

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Medical Imaging AI Interface with diagnostic annotations
1

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.

2

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.

3

Our Approach

1

PyTorch

Core framework powering the application architecture and user experience.

2

FastAPI

Essential technology enabling scalability and performance optimization.

3

DICOM Processing

Critical infrastructure component for data management and persistence.

4

AWS HealthLake

Supporting technology enhancing system capabilities and integration.

5

React

Additional tooling for monitoring, deployment, and operations.

4

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

96.5%
Diagnostic Accuracy
-60%
Diagnosis Time
500K+
Images Analyzed

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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