Enhancing Medical Imaging with Deep Learning: A Critical Review of Applications and Challenges

Abstract

Deep learning has rapidly and well transformed the cultural landscape of medical imaging, offering unprecedented accuracy, efficiency, as well as capabilities in diagnostic and therapeutic processes. This particular critical review explores some of the major applications of deep learning in medical imaging, including image classification, segmentation, and enhancement. Furthermore, it addresses the challenges that hinder the vast scientific adoption of these technologies, which consist of record shortages, model interpretability, and regulatory barriers. By synthesizing the latest advancements and analyzing contemporary challenges, this paper presents guidelines for future studies that are vital for the development of robust, ethical, and clinically effective deep learning solutions in medical imaging.