Machine learning in medicine / edited by Ayman El-Baz, Jasjit S. Suri.

Includes bibliographical references and index.

Machine Learning in Medicine explores the integration of advanced machine learning techniques into modern healthcare systems. The book focuses on how computational models—especially deep learning and neural networks—are applied to medical data for diagnosis, prediction, and clinical decision support. It highlights the role of computer-aided diagnosis (CAD) systems in detecting diseases such as cancer and neurological disorders, demonstrating how these tools improve accuracy and efficiency in clinical practice. The text also examines applications in areas like medical imaging, ultrasound diagnostics, dermatology, and cognitive decline detection, emphasizing real-world biomedical datasets and challenges. Additionally, the work addresses practical limitations of current machine learning models in healthcare, including data constraints and reliability issues, while proposing emerging solutions. Overall, the book presents both current trends and future directions in AI-driven medicine, aiming to support research and innovation in medical informatics.