The Effect Of Machine Learning On Image Interpretation In Radiology

Abstract

Machine learning (ML) has emerged as a transformative force in the field of radiology, significantly influencing image interpretation and diagnostic processes. This paper provides a comprehensive review of how machine learning algorithms, particularly those utilizing deep learning techniques, are reshaping radiological practices. It examines the integration of ML in image analysis, discusses its impact on diagnostic accuracy and workflow efficiency, and explores current challenges and future directions. By analyzing recent advancements and case studies, this review highlights the potential of ML to enhance diagnostic capabilities and improve patient outcomes in radiology.