The Impact Of Machine Learning On Image Interpretation In Radiology

Abstract

Machine learning (ML) has emerged as a transformative technology in radiology, significantly enhancing image interpretation and diagnostic accuracy. This paper explores the impact of ML on radiological practices, focusing on its applications, benefits, challenges, and future prospects. We review current literature and discuss how ML algorithms are integrated into various imaging modalities, improving disease detection, classification, and quantification. The integration of ML in radiology not only optimizes workflow efficiency but also holds promise for personalized medicine, paving the way for more accurate and timely patient care.