Deep Learning in Personalized Healthcare and Decision Support 1st Edition

Grab the future of medicine with Deep Learning in Personalized Healthcare and Decision Support, 1st Edition by Harish Garg and Jyotir Moy Chatterjee — a timely guide for anyone bridging artificial intelligence and patient care. This expertly written volume illuminates how deep learning transforms clinical decision support, precision medicine, and population health across global settings from India to North America and Europe.

Start with a clear, approachable overview of core deep learning concepts tailored to healthcare needs, then move into practical discussions on building and validating models using electronic health records, medical imaging, genomics, and wearable data. The authors balance technical rigor with clinical relevance, addressing model interpretability, bias mitigation, regulatory considerations, and real-world deployment challenges that hospitals, clinics, and health-tech teams face today.

Designed for clinicians, data scientists, healthcare administrators, and researchers, the book demonstrates how AI-driven decision support can enhance diagnosis accuracy, personalize treatment pathways, and optimize resource allocation. Real-world examples and case-driven explanations make complex topics accessible without sacrificing depth — ideal for professionals implementing AI solutions in both high-resource and resource-constrained environments.

Why this book matters: it connects cutting-edge deep learning research with actionable strategies for improving patient outcomes and operational efficiency. Whether you’re leading an AI initiative at a medical center, developing clinical algorithms, or studying healthcare informatics, this edition equips you with the knowledge to design robust, ethical, and effective decision-support systems.

Bring evidence-based AI into your practice or research. Order Deep Learning in Personalized Healthcare and Decision Support today and start translating algorithms into better care.

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