人工智能和机器学习在职业风险预防方面的进展:对预测风险建模和预防策略的系统审查
Pablo Armenteros-Cosme1, Marcos Arias-González1, Sergio Alonso-Rollán1
1BISITE Research Group, University of Salamanca, C. Espejo, 2, 37007 Salamanca, Spain.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
人工智能 (AI) 和机器学习 (ML) 越来越多地用于职业风险预防. 这次审查发现,人工智能在使用视觉数据检测危险方面表现出色,但需要多式联运数据和可解释模型来提高安全性.
科学领域:
- 职业健康和安全问题 职业健康和安全问题
- 工业中的人工智能
- 对工作场所危险的预测分析.
背景情况:
- 预防职业风险对工作场所安全至关重要,人工智能和机器学习正在成为关键工具.
- 该研究涉及人工智能的日益应用,用于预测和预防工作场所的危险和职业疾病.
- 现有的关于人工智能用于危险检测和风险预测的文献被系统地审查.
研究的目的:
- 识别,评估和综合有关人工智能算法的文献,用于检测和预测危险环境和职业风险.
- 专注于职业安全中的预测建模和预防策略.
- 评估人工智能应用在工作场所危险检测中的现状和局限性.
主要方法:
- 按照PRISMA 2020协议进行系统的文献审查,包括会议记录和技术报告.
- 在IEEE数字图书馆,PubMed,Scopus和Web of Science中对2019年以后发表的英语文章进行搜索.
- 严格选和对61篇选定的文章进行质量评估,不包括系统性审查和低质量的研究.
主要成果:
- 从2021-2024年,人工智能用于职业安全的出版物显著增长,其中中国,韩国和印度的贡献很大.
- 在建筑,采矿和制造业等高风险行业中确定的主要应用.
- 深度学习模型,特别是CNN和YOLO,分析摄像机的视觉数据 (超过40%的研究) 是主要的方法.
结论:
- 目前用于危险检测的AI系统受到过度依赖视觉数据和缺乏标准化的限制.
- 未来的研究应该整合多模式数据 (视觉,环境,生理) 并开发可解释的AI (XAI) 以提高准确性和信任度.
- 解决隐私和工人流离失所等社会影响需要透明的数据政策和监管框架.
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