Artificial Intelligence for Disease Diagnosis and Prognosis in Smart Healthcare 1st Edition

Capture the future of clinical care with Artificial Intelligence for Disease Diagnosis and Prognosis in Smart Healthcare, 1st Edition by — a clear, authoritative guide that bridges machine learning theory and real-world medical practice. This book draws you in with contemporary examples and practical strategies for applying AI to diagnostic workflows, risk stratification, and predictive modeling across diverse healthcare settings.

Explore in-depth coverage of core techniques—supervised and unsupervised learning, deep neural networks, explainable AI, and time-series analysis—paired with pragmatic discussions of data curation, model validation, and performance metrics. Written for clinicians, data scientists, health informaticians, and hospital administrators, the text balances technical rigor with accessible explanations so teams can evaluate, adopt, or scale AI solutions responsibly.

Benefit from chapters that tackle deployment challenges relevant to hospitals and clinics worldwide: integration with electronic health records, privacy-preserving methods, regulatory and ethical considerations, and strategies for low-resource and rural healthcare environments. Case studies illustrate outcomes in cardiology, oncology, radiology, and infectious disease surveillance, helping readers translate insights into safer, more efficient patient care.

Whether you’re leading a digital health initiative in North America, implementing clinical AI across Europe, or exploring smart healthcare innovations in Asia-Pacific and beyond, this 1st edition equips you with the knowledge to make informed decisions. Practical, forward-looking, and grounded in real practice, it’s an essential resource for anyone shaping the next generation of disease diagnosis and prognosis.

Order your copy today to stay at the forefront of smart healthcare innovation.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.