NeXt-Ray: Next Gen X-Ray diagnosis Using Deep Learning and GenAI

The incorporation of deep learning and generative artificial intelligence (GenAl) into chest radiography presents a transformative advancement in medical diagnostics, improving the interpretability and automation. Chest radiographs play a critical role in the detection of conditions such as Infiltration, Pneumothorax, Pneumonia, Hernia and Edema; however, their interpretation has historically necessitated considerable expertise and time. This study leverages deep learning to expedite and improve the accuracy of chest X-ray analysis, while utilizing GenAl, specifically through a GPT-based model, to automate the generation of structured, clinically relevant diagnostic reports. Grad-CAM (Gradient-weighted Class Activation Mapping) visualizations are incorporated within these reports to provide interpretability, highlighting regions of interest associated with identified pathologies. These insights are presented in a structured PDF, combining visual and textual data to support rapid diagnostic decisions. This integrated system demonstrates a scalable and robust solution for chest radiography analysis, poised to advance clinical workflows and support improved patient outcomes. The synergy between deep learning, GenAl, and interpretability tools holds immense potential to revolutionize diagnostic processes and enhance healthcare delivery.