{"product_id":"9789819239078","title":"Generative AI for Healthcare From Foundation Models to Clinical Integration","description":"\u003ch1\u003eGenerative AI for Healthcare\u003c\/h1\u003e\u003ch2\u003eFrom Foundation Models to Clinical Integration\u003c\/h2\u003e\u003ch3\u003eCarl Yang | Daniel Ting | Shawn Murphy\u003c\/h3\u003e\u003cdiv\u003e\u003cb\u003eComputers \/ Artificial Intelligence \/ General\u003c\/b\u003e\u003c\/div\u003e\u003cbr\u003e\u003cdiv\u003e\n\u003cp data-olk-copy-source=\"MessageBody\"\u003eAs generative artificial intelligence (AI) and foundation models rapidly transform healthcare, bridging the gap between algorithmic innovation and safe clinical integration has never been more critical. Generative AI for Healthcare provides a comprehensive, end-to-end guide to the next generation of AI in medicine and healthcare. Moving beyond traditional predictive analytics, this textbook explores how modern generative AI technology including large language models (LLMs), multimodality foundation models, and agentic frameworks are redefining clinical workflows, from automated medical documentation to personalized patient care.\u003c\/p\u003e\r\n\u003cp\u003eDesigned for data scientists, clinical informaticians, and medical researchers, the book systematically unpacks the transition from classical machine learning to state-of-the-art generative paradigms. Readers will explore the mechanics of deep learning architectures applied directly to complex healthcare data modalities like semi-structured Electronic Health Records (EHR), medical imaging, and continuous physiological signals. Crucially, the textbook emphasizes real-world application, offering actionable insights into clinical operations, workflow integration, regulatory frameworks, and the ethical deployment of AI to mitigate bias and data drift in live hospital environments and beyond.\u003c\/p\u003e\r\n\u003cp\u003eBy synthesizing technical rigor with clinical utility, this textbook equips readers with the practical skills to evaluate, deploy, and scale AI innovations that tangibly improve patient care. While a foundational understanding of basic statistics and healthcare data structures is recommended, the book’s intuitive framework, pairing technical concepts directly with concrete clinical input-to-output examples, makes advanced AI accessible. It is the essential blueprint for professionals preparing to lead the future of generative AI in healthcare.\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003cdiv\u003e\n\u003cstrong data-olk-copy-source=\"MailCompose\"\u003eDr. Carl Yang\u003c\/strong\u003e is a Tenured Associate Professor of Computer Science at Emory University, jointly appointed in the Schools of Medicine (BMI), Public Health (BIOS) and Nursing (CDS) at Emory University. He is also an Adjunct Professor in the School of Computational Science and Engineering in Georgia Tech, and an Honorary Visiting Professor in the AI in Medicine Institute of SingHealth Duke-NUS Academic Medical Center. His research focuses on machine learning, data mining, and AI for healthcare, with a particular emphasis on multimodal patient representation learning, clinical decision support, and translational AI systems for healthcare. Dr. Yang has published extensively in top AI and biomedical informatics venues and leads interdisciplinary research groups across computer science, medicine, and public health.\u003c\/div\u003e\r\n\u003cdiv\u003e \u003c\/div\u003e\r\n\u003cdiv\u003e\n\u003cstrong\u003eDr. Daniel Ting\u003c\/strong\u003e is a senior consultant vitreo-retinal surgeon at the Singapore National Eye Center, an Associate Professor at Duke-NUS Medical School, and an Adjunct Clinical Associate Professor and Innovation Mentor at Stanford University. He leads AI and digital innovation initiatives in ophthalmology as Director of the SingHealth AI Office and Head of AI and Digital Innovation at the Singapore Eye Research Institute. His research spans machine learning, deep learning, large language models, explainable AI, and the safe, ethical deployment of AI in clinical practice. Dr. Ting has an extensive publication record, including in high-impact journals such as JAMA, NEJM, Lancet, and Nature Medicine, and has received numerous international awards recognizing his contributions to AI and ophthalmology.\u003c\/div\u003e\r\n\u003cdiv\u003e \u003c\/div\u003e\r\n\u003cdiv\u003e\n\u003cstrong\u003eDr. Shawn Murphy\u003c\/strong\u003e currently serves as the Director of Research Computing and Informatics at Partners Healthcare and Associate Director for the Laboratory of Computer Science at the Massachusetts General Hospital where he developed the Research Patient Data Registry (RPDR) for Partners Healthcare. This application, which serves over 5,000 investigators performing research using the hospital medical record, served as the test bed for his work with Zak Kohane in developing the open source Informatics for Integrating Biology and the Bedside (i2b2) software platform now operating at over 120 hospitals worldwide. Murphy’s contribution as chief architect of the i2b2 platform has served to strengthen the understanding of the metabolic and genetic underpinnings of complex diseases by developing an informatics framework to integrate data for clinical research from electronic health records.\u003c\/div\u003e\n\u003c\/div\u003e\u003cbr\u003e\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublication Date: \u003c\/td\u003e\n\u003ctd\u003e09 November 2026\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublisher: \u003c\/td\u003e\n\u003ctd\u003eSpringer Nature Singapore\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eImprint: \u003c\/td\u003e\n\u003ctd\u003eSpringer\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eISBN-13: \u003c\/td\u003e\n\u003ctd\u003e9789819239078\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFormat: \u003c\/td\u003e\n\u003ctd\u003eHardback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e","brand":"Springer Nature Singapore","offers":[{"title":"Default Title","offer_id":51043024273548,"sku":"9789819239078","price":58.49,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9789819239078.jpg?v=1782525374","url":"https:\/\/lateknightbooks.com\/products\/9789819239078","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}