Machine learning (ML) transforms the landscape of modern healthcare, offering powerful tools to enhance diagnosis, care, and treatment across various medical specialties. In radiology, ML algorithms interpret medical imaging with accuracy, aiding in early detection of medical conditions and disorders. In pathology, ML analyzes complex tissue patterns and cellular structures, enabling more precise disease classification and reducing diagnostic variability. In cardiology, ML supports risk prediction, imaging interpretation, and real-time monitoring of heart conditions, contributing to improved patient outcomes. These applications present ML as indispensable when advancing clinical decision-making and personalized medicine.
Applications of Machine Learning in Radiology, Pathology, and Cardiology explores the utilization of intelligent technologies for medical treatment, diagnosis, and care. It examines the use of ML for diagnostic accuracy, efficiency, and patient care, and presents the challenges, limitations, and potential of ML applications in these critical medical fields. This book covers topics such as ethics and law, medical diagnosis, and oncology, and is a useful resource for medical and healthcare professionals, engineers, academicians, researchers, and scientists.