Published Paper Details:

The rapid advancement of Artificial Intelligence (AI) has revolutionized modern healthcare, with particular impact on medical imaging and radiological diagnostics. This paper presents the AI Radiology Co-Pilot, a comprehensive intelligent system developed to assist radiologists in the detection of chest X-ray abnormalities and the automated generation of structured diagnostic reports. The system employs a ResNet-50-based Convolutional Neural Network for accurate image classification, achieving robust detection of pathological conditions including pneumonia, effusion, and cardiomegaly. To facilitate structured report generation, the system integrates Mistral-7B-Instruct, a state-of-the-art Generative AI language model, which converts model predictions into coherent clinical reports and patient-friendly summaries. Additional features include a Grad-CAM-based explainability module for visual interpretation, a multilingual interactive chatbot for patient assistance, and a Flask-based web interface enabling real-time deployment. Experimental evaluation demonstrates significant improvements in diagnostic efficiency, report quality, and clinical communication. The proposed system bridges the gap between image-based classification and language-based report synthesis, offering a unified, interpretable, and accessible AI-powered radiology workflow.