This volume brings together cutting-edge research at the intersection of artificial intelligence, clinical care, and public health. While it highlights the impact of generative AI, including large language models, it also delves into broader challenges such as fairness, robustness, scalability, and explainability. Chapters explore: Applications of Generative AI in healthcare and medicine Strategies to reduce bias and improve equity in clinical AI Tools for making model predictions more explainable and accountable Approaches for real-world deployment at scale Human-centered and governance frameworks for responsible AI Rather than focusing on isolated use cases or technical performance alone, this book offers a systems-level perspective, bridging computational innovation with clinical and ethical relevance. Designed for researchers, healthcare professionals, and innovators, this collection provides critical insights for anyone aiming to responsibly develop or implement AI in health contexts.