AI Diagnostic Assistance for Radiology Department
MedVision Health
Client
MedVision Health
Industry
HealthTech
Services Used
Machine Learning, Product Engineering
Technologies
Python, PyTorch, MONAI, FastAPI
The Challenge
MedVision Health, a regional hospital network with 12 facilities, faced a critical shortage of radiologists. Report turnaround times averaged 48 hours, with critical findings sometimes delayed by 24 hours or more. Patient care was being directly impacted by delayed diagnoses.
The radiology department processed over 2,000 imaging studies per day across X-ray, CT, and MRI modalities. With only 15 radiologists covering all facilities, the workload was unsustainable. Burnout was high, and the hospital was struggling to recruit additional radiologists in a competitive market.
MedVision needed an AI system that could pre-screen imaging studies, prioritize critical cases for immediate review, and assist radiologists with preliminary findings — all while meeting strict HIPAA compliance and clinical safety requirements.
Our Solution
Fastlab AI developed a comprehensive AI diagnostic assistance system built on deep learning models trained on over 500,000 anonymized imaging studies. The system was designed to augment (not replace) radiologists, serving as an intelligent triage and decision support tool.
We built separate specialized models for chest X-ray analysis (detecting pneumonia, cardiomegaly, pleural effusion, and 12 other conditions), CT head analysis (hemorrhage, stroke, masses), and musculoskeletal X-ray analysis (fractures). Each model was validated against gold-standard radiologist annotations.
The system automatically prioritized the worklist by flagging studies with critical findings (e.g., hemorrhage, pneumothorax) and moving them to the top of the queue. It provided preliminary AI annotations that radiologists could accept, modify, or reject — with each interaction feeding back into model improvement.
We ensured full HIPAA compliance with on-premise deployment, DICOM integration with the existing PACS system, and a comprehensive audit trail. The system underwent rigorous clinical validation with a prospective study before deployment.
Technologies Used
Python
PyTorch
●
MONAI
FastAPI
React
PostgreSQL
AWS
Docker
●
DICOM
Results
Measurable impact delivered
Diagnostic Accuracy
Faster Reporting
Scans Processed
Critical Misses
“Working with Fastlab AI on our computer vision system was an incredible experience. They took our vague requirements and turned them into a diagnostic tool that achieves 95% accuracy. True AI expertise.”
Dr. Rajesh Kapoor
Chief Medical Officer, MedVision Health
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Selected screens and implementation highlights
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