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Large language models illuminate a progressive pathway to artificial intelligent healthcare assistant
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作者 Mingze Yuan Peng Bao +9 位作者 Jiajia Yuan yunhao shen Zifan Chen Yi Xie Jie Zhao Quanzheng Li Yang Chen Li Zhang Lin shen Bin Dong 《医学+(英文)》 2024年第2期102-124,共23页
With the rapid development of artificial intelligence,large language models(LLMs)have shown promising capabilities in mimicking human-level language comprehen-sion and reasoning.This has sparked significant interest i... With the rapid development of artificial intelligence,large language models(LLMs)have shown promising capabilities in mimicking human-level language comprehen-sion and reasoning.This has sparked significant interest in applying LLMs to enhance various aspects of healthcare,ranging from medical education to clinical decision support.However,medicine involves multifaceted data modalities and nuanced reasoning skills,presenting challenges for integrating LLMs.This review introduces the fundamental applications of general-purpose and specialized LLMs,demon-strating their utilities in knowledge retrieval,research support,clinical workflow automation,and diagnostic assistance.Recognizing the inherent multimodality of medicine,the review emphasizes the multimodal LLMs and discusses their ability to process diverse data types like medical imaging and electronic health records to augment diagnostic accuracy.To address LLMs'limitations regarding personalization and complex clinical reasoning,the review further explores the emerging develop-ment of LLM-powered autonomous agents for healthcare.Moreover,it summarizes the evaluation methodologies for assessing LLMs'reliability and safety in medical contexts.LLMs have transformative potential in medicine;however,there is a pivotal need for continuous optimizations and ethical oversight before these models can be effectively integrated into clinical practice. 展开更多
关键词 Large language models Artificial intelligence Medicine Healthcare assistant Prompt engineering In-context learning
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