AI-Powered Medical Imaging Analysis for Pilot Deployment

Executive Summary

We collaborated with a radiology department at a hospital to pilot an AI-powered system for chest X-ray analysis. The solution focused on detecting pneumonia and lung nodules using computer vision, improving diagnostic accuracy, reducing radiologist workload, and accelerating patient care. This project served as a proof of concept for integrating AI into medical imaging workflows with minimal disruption.

Problem Statement

The hospital faced challenges in managing an increasing volume of chest X-rays, leading to delayed diagnoses, occasional errors, and inefficiencies in prioritizing critical cases. The goal was to develop a scalable AI solution to address these issues and enhance workflow efficiency.

Solution Overview

We developed a deep learning-based solution using a fine-tuned CNN model (ResNet-50) to detect abnormalities in chest X-rays. The pipeline included image preprocessing, model training, and cloud deployment, seamlessly integrated with the hospital’s system. The system provided automated detection, severity scoring, and prioritized reporting to support radiologists.