name2---->Kai-Cheng Hsu(字串長度:13)---->Edward Kai-Cheng Hu(字串長度:19)```anchor---->kai-cheng-hsu(字串長度:13)---->edward-kai-cheng-hu(字串長度:19)```talk_title---->(字串長度:0)---->Agentic LLM and Smart Healthcare(字串長度:32)```talk_title2---->(字串長度:0)---->代理大型語言模型與智慧醫療(字串長度:39)```desc---->(字串長度:0)---->Abstract: This presentation delves into the forefront of medical artificial intelligence, particularly focusing on the role of agentic Large Language Models (LLMs) in transforming healthcare. These advanced models exhibit agency by autonomously generating realistic and contextually appropriate medical data and interventions, marking a shift from theoretical exploration to tangible, real-world applications. This evolution emphasizes the need for a unified, interdisciplinary approach.
The session starts with an analysis of how agentic LLMs have been seamlessly integrated into existing healthcare frameworks, notably enhancing the accuracy and efficiency of disease prognosis and diagnosis. Specific case studies, such as the use of these models in diagnosing acute ischemic stroke, highlight how agentic LLMs are instrumental in critical health domains. Acute ischemic stroke, a leading cause of disability and mortality worldwide, requires rapid and accurate diagnosis to initiate timely intervention, which can significantly reduce long-term disabilities and improve outcomes. This illustrates the crucial role of agentic LLMs in improving diagnostic accuracy and refining medical decision-making processes.
The objective of this presentation is to provide a rigorous academic perspective on the potential and current applications of agentic LLMs in healthcare. It aims to foster a comprehensive understanding of their impact on the future trajectory and developmental prospects of medical AI.
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