Medical imaging plays a pivotal role in modern healthcare by providing valuable insights into the human body's internal structures and functions. With the advent of deep learning techniques, there has been a paradigm shift in the way radiological images are analyzed and interpreted. This research paper explores the application of deep learning in enhancing diagnostic accuracy and workflow efficiency through automated detection and classification of pathologies in radiological images. Through a comprehensive literature review, the evolution of deep learning in medical imaging is examined, highlighting its transformative impact on the field. Previous studies on automated detection and classification of pathologies are analyzed, emphasizing the significant strides made in improving diagnostic capabilities. The methodology section outlines the architecture design and evaluation metrics used for automated detection and classification tasks. Case studies and examples demonstrate the effectiveness of deep learning models in accurately identifying various pathologies, surpassing traditional methods in performance. Furthermore, the paper assesses the impact of deep learning on diagnostic accuracy and workflow efficiency. Quantitative analysis reveals substantial improvements in diagnostic accuracy, along with reductions in radiologist workload and interpretation time. Ethical considerations such as patient privacy, regulatory compliance, and algorithmic bias are also discussed.