The healthcare industry is undergoing a transformative shift as artificial intelligence moves from isolated predictive models to autonomous, goal-driven agents capable of reasoning, planning, and adapting in real time.
Agentic AI for Healthcare: Building Scalable Models for Production is the first comprehensive technical guide that focuses specifically on designing and deploying agentic AI systems within complex clinical and operational healthcare environments. Unlike traditional AI systems that passively infer outcomes, agentic AI involves systems that actively make decisions, coordinate with other agents, and take initiative toward long-term goals, making them ideal for dynamic and high-stakes healthcare settings. What sets this book apart is its deep technical focus tailored for computer scientists, architects, and AI engineers who are building real-world healthcare solutions. Each chapter bridges theoretical foundations with production-ready implementations using cloud-native technologies, distributed architectures, knowledge representation models, and secure deployment pipelines. With detailed case studies in oncology, vaccine management, and hospital operations, the book demonstrates how agentic AI can improve care delivery, reduce cognitive burden on clinicians, and automate routine workflows – all while meeting regulatory standards.
As healthcare increasingly adopts AI-first strategies, this book fills a critical need by offering a structured, engineering-centered approach to building scalable, intelligent, and trustworthy healthcare systems.
