Artificial intelligence (AI) is evolving rapidly, moving from predictive systems toward a new era dominated by generative technologies. Among the most influential advances are language models, which have expanded into diverse forms capable of addressing increasingly complex and domain-specific challenges. One of the most transformative developments is the emergence of vision language models (VLMs), which integrate visual and textual understanding to enable richer forms of analysis and interaction. By learning relationships between images and text through large-scale training, these models open new possibilities for applications that require deeper contextual interpretation of visual data. In healthcare, VLMs are particularly promising, supporting innovations such as visual question answering, automated medical report interpretation, and enhanced diagnostic and analytical capabilities. Vision Language Models for Next-Generation Healthcare provides one of the first comprehensive explorations of VLMs in healthcare, highlighting how their multimodal capabilities can transform diagnostics, clinical decision-making, and patient care. By combining insights from computer science, biomedical engineering, and clinical practice, this book offers a unified reference on VLMs tailored for healthcare. Covering topics such as augmented report generation, radiology, and workflow intelligence, this book is a critical emerging resource for graduate and doctoral students, computer scientists, biomedical engineers, healthcare informatics, medical researchers, and more.