Leveraging AI and ML in Cardiovascular Diagnosis and Management examines how artificial intelligence and machine learning transform cardiovascular care. It gathers chapters from leading researchers to bridge clinical practice with cutting-edge analytics, imaging, and decision support. The volume details core topics across the field, including fundamentals of AI/ML, AI-driven cardiovascular imaging, predictive analytics for risk stratification, heart failure management, arrhythmia detection, wearable-enabled monitoring, and the integration of multi-omics with clinical data. It also addresses neurocardiology, clinical decision support systems, ethics, and regulatory considerations, all framed for cardiovascular scientists and clinicians. For academics, clinicians, and educators, the book offers actionable workflows, a cohesive framework for translating AI and ML advances into improved diagnosis, treatment personalization, and patient outcomes across diverse populations.
- Provides validated workflows for integrating AI/ML into cardiovascular practice, including data processing, model validation, and clinical decision support
- Offers case-based guidance and real-world examples spanning imaging, risk stratification, and treatment optimization
- Delivers a global, interdisciplinary contributor network that ensures diverse perspectives and up-to-date techniques in cardiovascular AI
