大型医学语言模型:调查

IF 3.1 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Machine Learning and Cybernetics Pub Date : 2024-08-19 DOI:10.1007/s13042-024-02318-w
Yanxin Zheng, Wensheng Gan, Zefeng Chen, Zhenlian Qi, Qian Liang, Philip S. Yu
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引用次数: 0

摘要

为了应对数字经济时代数字智能领域的挑战,人们开发了大型语言模型(LLM)。计算能力和可用资源的提高极大地推动了大型语言模型的发展,使其能够融入人类生活的各个领域。医学 LLM 是重要的应用工具,在各种医疗场景中都具有潜力。在本文中,我们将回顾 LLM 的发展,重点关注医学 LLM 的需求和应用。我们简明扼要地概述了现有模型,旨在探索先进的研究方向,让研究人员在未来的医疗应用中受益。我们强调了医学 LLM 在应用中的优势,以及在开发过程中遇到的挑战。最后,我们提出了减轻挑战的技术整合方向和未来医学 LLM 的潜在研究方向,旨在更好地满足医学领域的需求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Large language models for medicine: a survey

To address challenges in the digital economy’s landscape of digital intelligence, large language models (LLMs) have been developed. Improvements in computational power and available resources have significantly advanced LLMs, allowing their integration into diverse domains for human life. Medical LLMs are essential application tools with potential across various medical scenarios. In this paper, we review LLM developments, focusing on the requirements and applications of medical LLMs. We provide a concise overview of existing models, aiming to explore advanced research directions and benefit researchers for future medical applications. We emphasize the advantages of medical LLMs in applications, as well as the challenges encountered during their development. Finally, we suggest directions for technical integration to mitigate challenges and potential research directions for the future of medical LLMs, aiming to meet the demands of the medical field better.

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来源期刊
International Journal of Machine Learning and Cybernetics
International Journal of Machine Learning and Cybernetics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
7.90
自引率
10.70%
发文量
225
期刊介绍: Cybernetics is concerned with describing complex interactions and interrelationships between systems which are omnipresent in our daily life. Machine Learning discovers fundamental functional relationships between variables and ensembles of variables in systems. The merging of the disciplines of Machine Learning and Cybernetics is aimed at the discovery of various forms of interaction between systems through diverse mechanisms of learning from data. The International Journal of Machine Learning and Cybernetics (IJMLC) focuses on the key research problems emerging at the junction of machine learning and cybernetics and serves as a broad forum for rapid dissemination of the latest advancements in the area. The emphasis of IJMLC is on the hybrid development of machine learning and cybernetics schemes inspired by different contributing disciplines such as engineering, mathematics, cognitive sciences, and applications. New ideas, design alternatives, implementations and case studies pertaining to all the aspects of machine learning and cybernetics fall within the scope of the IJMLC. Key research areas to be covered by the journal include: Machine Learning for modeling interactions between systems Pattern Recognition technology to support discovery of system-environment interaction Control of system-environment interactions Biochemical interaction in biological and biologically-inspired systems Learning for improvement of communication schemes between systems
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