AI-Powered Medical Imaging Analysis for Radiology Efficiency

Healthcare

Client

New York, USA

location

Client Overview:

A radiology unit processing thousands of scans monthly needed a way to reduce diagnostic turnaround time while maintaining accuracy. Traditional methods couldn’t keep pace with demand.

Challenge:

Manual imaging analysis was time-intensive, prone to fatigue-related errors, and caused diagnostic delays.

RetailCo needed a solution to automate these tasks, reduce response times, and improve customer satisfaction without sacrificing the quality of support.

Solution:

We developed and deployed an AI co-pilot for imaging analysis. Features included:

Pattern recognition for anomalies in X-rays, MRIs, and CT scans
Image segmentation and comparison against historical data
AI-flagging of critical cases for priority review

Technology Stack

Computer Vision: Custom CNN architectures
Model Training: Public and private radiology datasets
Integration: PACS and DICOM standards

Implementation Timeline

Month 1: Model training and benchmarking
Month 2: Shadow deployment and clinician review
Month 3: Full integration with radiology workflow

Client Testimonial:

Our AI imaging solution has become an essential member of our radiology team. It flags things we sometimes miss and speeds up everything.

Results

40% faster scan interpretation
93% accuracy in detecting abnormalities
80% adoption rate among radiologists