医学大语言模型和多模态大语言模型的综合研究

IF 14.7 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Information Fusion Pub Date : 2024-12-23 DOI:10.1016/j.inffus.2024.102888
Hanguang Xiao, Feizhong Zhou, Xingyue Liu, Tianqi Liu, Zhipeng Li, Xin Liu, Xiaoxuan Huang
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引用次数: 0

摘要

自ChatGPT和GPT-4发布以来,大型语言模型(llm)和多模态大型语言模型(mllm)因其在理解、推理和生成方面的卓越能力而受到广泛关注,为将人工智能集成到医学中引入了变革范例。本调查全面概述了医学法学硕士和法学硕士的发展、原理、应用场景、挑战和未来方向。具体来说,它首先检查范式转变,追踪从传统模型到llm和mllm的转变,并强调这些llm和mllm在医学应用中的独特优势。其次,调查回顾了现有的医学法学硕士和mllm,为其建设和评估提供了清晰、系统的详细指导。随后,为了强调llm和mllm在医疗保健领域的巨大价值,调查探讨了该领域的五个有前途的应用。最后,调查解决了医学法学硕士和法学硕士面临的挑战,并提出了实用的策略和未来的方向,使其融入医学。总之,这项调查提供了医学法学硕士和法学硕士的技术方法和实际临床应用的全面分析,旨在弥合这些先进技术与临床实践之间的差距,从而促进下一代智能医疗保健系统的发展。
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A comprehensive survey of large language models and multimodal large language models in medicine
Since the release of ChatGPT and GPT-4, large language models (LLMs) and multimodal large language models (MLLMs) have attracted widespread attention for their exceptional capabilities in understanding, reasoning, and generation, introducing transformative paradigms for integrating artificial intelligence into medicine. This survey provides a comprehensive overview of the development, principles, application scenarios, challenges, and future directions of LLMs and MLLMs in medicine. Specifically, it begins by examining the paradigm shift, tracing the transition from traditional models to LLMs and MLLMs, and highlighting the unique advantages of these LLMs and MLLMs in medical applications. Next, the survey reviews existing medical LLMs and MLLMs, providing detailed guidance on their construction and evaluation in a clear and systematic manner. Subsequently, to underscore the substantial value of LLMs and MLLMs in healthcare, the survey explores five promising applications in the field. Finally, the survey addresses the challenges confronting medical LLMs and MLLMs and proposes practical strategies and future directions for their integration into medicine. In summary, this survey offers a comprehensive analysis of the technical methodologies and practical clinical applications of medical LLMs and MLLMs, with the goal of bridging the gap between these advanced technologies and clinical practice, thereby fostering the evolution of the next generation of intelligent healthcare systems.
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来源期刊
Information Fusion
Information Fusion 工程技术-计算机:理论方法
CiteScore
33.20
自引率
4.30%
发文量
161
审稿时长
7.9 months
期刊介绍: Information Fusion serves as a central platform for showcasing advancements in multi-sensor, multi-source, multi-process information fusion, fostering collaboration among diverse disciplines driving its progress. It is the leading outlet for sharing research and development in this field, focusing on architectures, algorithms, and applications. Papers dealing with fundamental theoretical analyses as well as those demonstrating their application to real-world problems will be welcome.
期刊最新文献
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