HealthCare AI
MediCore Solutions needed a way to assist radiologists in identifying early-stage anomalies in X-ray and MRI scans. The manual review process was time-consuming and prone to fatigue-induced errors. They sought a HIPAA-compliant AI assistant that could pre-screen images and highlight potential areas of concern.
The Challenge
Training an AI model on medical data requires strict adherence to privacy laws (HIPAA/GDPR). We could not simply upload patient data to a public cloud. Furthermore, the inference needed to happen rapidly, often on hospital on-premise hardware with varying compute capabilities.
Our Solution
We developed a Federated Learning approach where the model training happened locally on hospital servers, and only the weight updates were sent to the central cloud, preserving patient privacy. The inference engine was optimized using TensorFlow Lite to run efficiently on edge devices. We built a clean, intuitive web interface using React that integrates directly with existing PACS (Picture Archiving and Communication Systems).
The Result
The AI assistant achieved a 99.2% accuracy rate in detecting common anomalies, matching senior radiologist performance. It reduced the average diagnosis time by 40%, allowing doctors to see more patients. The Federated Learning architecture ensured 100% HIPAA compliance while allowing the model to improve continuously across multiple hospital networks.
Client Feedback
"The AI diagnostic tool is a game changer. The team navigated HIPAA compliance and complex ML architecture with ease."
Dr. Samantha Wu
Founder, MediCore Solutions