4 New Medical Computer Applications Books Shaping 2025

Drawing from their expertise in medical imaging and machine learning, Xiaoxiao Li, Ziyue Xu, and Huazhu Fu offer a focused exploration of federated learning (FL) tailored specifically for medical imaging challenges. You learn how FL enables multiple healthcare institutions to collaboratively build accurate machine learning models without exchanging sensitive patient data, addressing privacy and data heterogeneity issues head-on. Practical chapters guide you through the architecture, algorithms, and real-world implementations, including case studies illustrating FL’s impact on medical diagnostics. If you are a computer scientist, engineer, clinician, or policymaker navigating the complexities of medical AI, this book equips you with the knowledge to leverage FL effectively while understanding its limitations and infrastructure demands.

Alex Khang, drawing on his expertise in computational intelligence, breaks down the complex interplay between AI and IoT within smart healthcare systems. You’ll gain a clear understanding of how connected devices can monitor health indicators in real-time and assist medical professionals in decision-making through AI-enhanced data processing. The book explores the foundational physics behind AI-collaborative IoT, making it accessible without sacrificing depth, and tackles ethical concerns like privacy and security that arise with these technologies. If you’re interested in how future healthcare tech will evolve and want practical insight into its broader applications beyond medicine, this book will serve you well.

This tailored book explores the rapidly evolving landscape of medical AI and privacy as it stands in 2025, focusing on the latest developments and innovative techniques reshaping healthcare technology. It covers emerging AI models, privacy-preserving approaches such as federated learning, and the integration of data security with cutting-edge medical applications. By matching your background and interests, the content delves into topics that matter most to you, providing a guided journey through new research findings and forward-looking insights. This personalized exploration helps you stay ahead in understanding how AI and privacy interplay to transform medical data handling and patient care.

After examining the evolving landscape of clinical research informatics, Rachel L. Richesson and her co-authors offer a comprehensive overview of how informatics integrates with modern clinical research. You’ll gain insight into the shifting role of consumers and how clinical care and research are becoming intertwined within global healthcare systems. The book delves into practical challenges faced by biomedical informatics professionals and explores frameworks that support this dynamic field, including detailed discussions on data management and regulatory environments. It’s a solid resource if you’re aiming to understand the current and future state of clinical research informatics, especially if you work in healthcare IT or research administration.

What happens when deep learning meets medical imagery? Dr. Teik Toe Teoh explores this intersection by applying convolutional neural networks (CNNs) to critical healthcare challenges like brain tumor and skin cancer classification. You gain not just an overview of CNN fundamentals but also insight into specific techniques such as data augmentation and image processing that elevate model accuracy. Each chapter contextualizes medical conditions alongside AI applications, making it useful whether you’re a researcher eyeing future problems or a practitioner curious about AI’s practical impact. This book suits those ready to grasp how advanced neural networks can transform diagnostics through medical imaging.