职业与健康 ›› 2025, Vol. 41 ›› Issue (24): 3447-3451.

• 综述 • 上一篇    下一篇

大语言模型在职业病防治中的应用前景

金佳纯1, 王绮婷2, 夏冰1, 王姝1, 杨爱初1, 程伟彬3   

  1. 1 广东省职业病防治院职业健康监护所广东 广州 510300
    2 广东药科大学公共卫生学院广东 广州 510310
    3 广东省第二人民医院人工智能医疗应用研究所广东 广州 510320
  • 收稿日期:2025-04-12 修回日期:2025-04-28 出版日期:2025-12-15 发布日期:2026-01-22
  • 作者简介:金佳纯,女,副主任医师,主要从事职业健康检查与职业病诊断工作。
  • 基金资助:
    广东省医学科研基金项目(B2025825);广东省医学科研基金项目(C2025042)

Application prospects of large language model in occupational disease prevention and treatment

JIN Jiachun1, WANG Qiting2, XIA Bing1, WANG Shu1, YANG Aichu1, CHENG Weibin3   

  1. 1 Department of Occupational Health SurveillanceGuangdong Province Hospital for Occupational Disease Prevention and Treatment,GuangzhouGuangdong 510300, China
    2 School of Public HealthGuangdong Pharmaceutical University,GuangzhouGuangdong 510310, China
    3 Institute of Artificial Intelligence Healthcare ApplicationGuangdong Second Provincial General Hospital,GuangzhouGuangdong 510320, China
  • Received:2025-04-12 Revised:2025-04-28 Online:2025-12-15 Published:2026-01-22

摘要:

随着工业化进程的加快,职业病问题日益突出,劳动者健康面临严重威胁。传统职业病防治手段存在数据整合不充分、诊断效率低、预防措施缺乏针对性等问题,而大语言模型(large language model,LLM)是一种基于深度学习技术的人工智能模型,可通过海量数据深度分解与训练,实现对自然语言的理解和逻辑生成。面对繁重的职业病防控任务,大语言模型可为优化传统职业病防治提供新的可能性。大语言模型通过整合海量数据,促进职业病智能诊断和预警,并为劳动者群体提供个性化的职业健康教育和培训,有望在职业病防治领域带来革命性的变革。本文旨在探讨大语言模型在职业病防治中的应用优势、预期应用场景及其可能面临的挑战,并提出未来展望。

关键词: 大语言模型, 职业病防治, 优势, 挑战

Abstract:

With the acceleration of industrialization,occupational diseases have become increasingly prominent,posing a serious threat to the health of workers. Traditional methods for the prevention and treatment of occupational diseases face challenges such as insufficient data integration,low diagnostic efficiency,and lack of targeted preventive measures. Large language model(LLM),which is artificial intelligence model based on deep learning technology,can understand natural language and generate logical content through the deep analysis and training of massive amounts of data. Faced with the heavy task of occupational disease prevention and treatment,LLM offers new possibilities for optimizing traditional occupational disease prevention and treatment. By integrating vast amounts of data,LLM can facilitate intelligent diagnosis and early warning of occupational diseases and provide personalized occupational health education and training for workers,potentially bringing revolutionary changes to the field of occupational disease prevention and treatment. This paper aims to explore the application advantages of LLM in occupational disease prevention and treatment,its anticipated application scenarios,the challenges it may face,and propose future prospects.

Key words: Large language model, Occupational disease prevention and treatment, Advantage, Challenge

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