AI to Analyzing Medical Images and Diagnosis

Over the past decade, in thefield of medical image analysis, there has been a significant evolution due to the emergence of the artificial intelligence, particularly in deep learning. Traditional diagnostic methods often depend on the expertise of radiologists. This paper addresses the challenge of automating the diagnostic process from radiographic images to enhance accuracy and accessibility in clinical decision-making. The proposed solution leverages convolutional neural networks (CNNs), fine-tuned through transfer learning, to classify and detect abnormalities in medical images. The architecture supports real-time deployment in hospitals and remote healthcare systems, offering scalable and explainable diagnostic support. Future extensions will explore multi-modal integration and live clinical validation.