Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Varghese, Nisha Title A name given to the resource Revolutionizing Healthcare: The Impact of Generative AI and Large Language Models Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Generative Intelligence in Healthcare: Transforming Patient Care with AI Creativity;pp.75-90 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003539483-4" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003539483-4</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105003051308?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105003051308?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Varghese N., Department of Computer Science, Christ University, India Description An account of the resource The chapter explores the transformative impact of generative AI and large language models (LLMs) in healthcare, emphasizing their potential to revolutionize patient care, clinical operations, and medical research. Generative AI, a subset of artificial intelligence, offers groundbreaking capabilities such as personalized medicine, virtual health assistants, and enhanced diagnostic accuracy. LLMs like Med-PaLM and BioBERT are fine-tuned to perform specific healthcare tasks, such as clinical note summarization and diagnostic support. These models also assist in drug discovery, clinical trials, and pandemic preparedness by analyzing complex medical data and predicting patient outcomes with high accuracy. The chapter also addresses the ethical and regulatory considerations associated with AI in healthcare, including data privacy, bias, and accountability. While the integration of AI technologies promises significant advancements, it also requires stringent regulatory oversight to ensure safety, efficacy, and fairness. The potential of generative AI to generate synthetic medical data offers a secure way to advance research without compromising patient privacy. Additionally, AI can optimize healthcare processes, enhance patient engagement, and accelerate medical research, contributing to a more efficient and personalized healthcare system. The chapter concludes by highlighting the need for continuous collaboration between AI developers, healthcare professionals, and regulators to maximize the benefits of these technologies while addressing the associated risks. 2025 selection and editorial matter, Sakshi Gupta, Umesh Gupta, Moolchand Sharma, Kamal Malik; individual chapters, the contributors. Publisher An entity responsible for making the resource available CRC Press Relation A related resource ISBN: 978-104032974-0; 978-103288740-1; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online