摘要
深度学习是人工智能领域的热门研究方向之一,它通过构建多层人工神经网络模仿人脑对数据的处理机制。大型语言模型(large language model,LLM)基于深度学习的架构,在无需编程指令的情况下,能通过分析大量数据以获得理解和生成人类语言的能力,被广泛应用于自然语言处理、计算机视觉、智慧医疗、智慧交通等诸多领域。文章总结了LLM在医疗领域的应用,涵盖了LLM针对医疗任务的基本训练流程、特殊策略以及在具体医疗场景中的应用。同时,进一步讨论了LLM在应用中面临的挑战,包括决策过程缺乏透明度、输出准确性以及隐私、伦理问题等,随后列举了相应的改进策略。最后,文章展望了LLM在医疗领域的未来发展趋势,及其对人类健康事业发展的潜在影响。
Deep learning(DL)is a popular research area in artificial intelligence.It simulates the data processing mechanism of the human brain by constructing multilayer artificial neural networks.Large language models(LLMs)based on the DL architecture can understand and generate human language by analyzing enormous data without programming instructions.Thus,LLMs are widely employed in various domains,such as natural language processing,computer vision,intelligent healthcare,and intelligent transportation.This article summarizes the application of LLMs in the healthcare sector,exploring their basic training processes,specific strategies for executing healthcare tasks,and their applications in specific healthcare scenarios.It also discusses the challenges of applying LLMs to the healthcare field,including the lack of transparency in decision-making processes,the accuracy of the output contents,and issues related to privacy and ethics.Thereafter,several strategies for addressing these issues are discussed.Finally,the future development trends of LLM in healthcare,as well as its criticality in promoting human health,are discussed.
作者
肖建力
许东舟
王浩
刘敏
周雷
朱林
顾松
XIAO Jianli;XU Dongzhou;WANG Hao;LIU Min;ZHOU Lei;ZHU Lin;GU Song(School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China;Department of Cardiothoracic Surgery,Shanghai Children’s Medical Center,School of Medicine,Shanghai Jiao Tong University,Shanghai 200127,China;Department of Gynecology of Integrated Traditional Chinese and Western Medicine,Obstetrics and Gynecology Hospital of Fudan University,Shanghai 200011,China;School of Health Science and Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China;Trauma Center,Shanghai General Hospital,Shanghai 201620,China)
出处
《智能系统学报》
北大核心
2025年第3期530-547,共18页
CAAI Transactions on Intelligent Systems
基金
国家自然科学基金项目(61603257)。
