04595nam a22005895i 4500 978-3-031-75771-6 DE-He213 20251030114943.0 cr nn 008mamaa 250219s2024 sz | s |||| 0|eng d 9783031757716 978-3-031-75771-6 10.1007/978-3-031-75771-6 doi R856-857 MQW bicssc TEC059000 bisacsh MQW thema Revolutionizing Healthcare 5.0: The Power of Generative AI [electronic resource] : Advancements in Patient Care Through Generative AI Algorithms / edited by Pronaya Bhattacharya, Haipeng Liu, Pushan Kumar Dutta, Joel J. P. C. Rodrigues, Gautam Sethi. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. IX, 476 p. 165 illus., 131 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Studies in Systems, Decision and Control, 2198-4190 ; 571 The emerging healthcare landscape -- AI for Healthcare 5.0: A shift towards personalized healthcare -- Transition to Generative AI in Healthcare 5.0 -- Drug Synthesis through Generative AI Models -- Generative AI for Data Augmentation in Clinical Trials -- Generative AI models for Personalized Healthcare -- Generative Deep Models for Medical Imaging -- Generative AI-driven fitness and real-time healthcare monitoring and control -- The future of Surgery and Anatomical Operations via Generative AI -- Generative models for AR/VR assisted Healthcare 5.0 -- Accuracy and Reliability of Synthetic Data Generation. This book serves as a critical resource that bridges the gap between burgeoning technology and its practical implementation. The book starts with an in-depth exploration of healthcare 5.0 principles, laying the foundation for the reader to understand the current shifts in healthcare paradigms. Then, it dives into the intricacies of generative models in healthcare, detailing how these algorithms work and the applications they serve. The book further delves into the subsets of generative machine learning and deep learning techniques in healthcare. As we move towards more complex applications, the book takes a turn to address the critical subject of interpretability and explainability in generative models, a topic that resonates profoundly given the life-critical nature of medical decisions. Finally, the book concludes with a robust discussion on the security and privacy concerns that accompany the deployment of GAI in real healthcare settings. By offering a multidimensional viewpoint—coupled with case studies, statistical analyses, and expert insights—the book ensures that the reader is left with a nuanced understanding of how GAI can be both a boon and a challenge in healthcare. As such, the proposed book serves as an indispensable resource for healthcare professionals, data scientists, researchers, and anyone invested in the future of healthcare and AI. Biomedical engineering. Computational intelligence. 64052 Artificial intelligence. 58441 Medical and Health Technologies. Computational Intelligence. 64052 Artificial Intelligence. 58441 Bhattacharya, Pronaya. editor. edt http://id.loc.gov/vocabulary/relators/edt 82770 Liu, Haipeng. editor. (orcid)0000-0002-4212-2503 https://orcid.org/0000-0002-4212-2503 edt http://id.loc.gov/vocabulary/relators/edt 90850 Dutta, Pushan Kumar. editor. edt http://id.loc.gov/vocabulary/relators/edt 82769 Rodrigues, Joel J. P. C. editor. edt http://id.loc.gov/vocabulary/relators/edt 68912 Sethi, Gautam. editor. edt http://id.loc.gov/vocabulary/relators/edt 88133 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031757709 Printed edition: 9783031757723 Printed edition: 9783031757730 Studies in Systems, Decision and Control, 2198-4190 ; 571 https://doi.org/10.1007/978-3-031-75771-6 ZDB-2-SME ZDB-2-SXM 66731 66731