{"title":"The role of artificial intelligence in occupational health in radiation exposure: a scoping review of the literature.","authors":"Zohreh Fazli, Mehran Sadeghi, Mohebat Vali, Parvin Ahmadinejad","doi":"10.1186/s12940-025-01186-3","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>Artificial intelligence (AI) has the potential to significantly enhance workplace safety and mitigate occupational radiation exposure risks by improving the accuracy of assessment and management of these hazards. This study aims to review research on the use of AI in the assessment, monitoring, control, and protection of occupational radiation exposure.</p><p><strong>Method: </strong>This review was conducted according to the PRISMA guidelines. A comprehensive search was performed in the Web of Science, Scopus, and PubMed databases from inception to April 2024. The search strategy was designed based on the PICO principle and included keywords related to artificial intelligence, occupational exposure, radiation, and industry. The inclusion criteria explored the application of artificial intelligence in the assessment, monitoring, control, and protection against occupational radiation exposure. The quality of the included studies was evaluated using the MMAT critical appraisal tool.</p><p><strong>Result: </strong>In this review, the initial literature search in the Web of Science, Scopus, and PubMed databases identified 2920 articles. After removing duplicate references, screened based on title, keywords, and abstract, Ultimately, 59 eligible articles were selected, which utilized various artificial intelligence tools, such as expert systems, machine learning, deep learning, and other applied AI models. Of all the articles, 76% had high scores and were considered strong. These studies were categorized into three groups: supervision and assessment, detection and monitoring, protection, control, and personal protective equipment.</p><p><strong>Conclusion: </strong>The successful application of AI can potentially improve occupational radiation exposure management, but several key challenges must be addressed. These include the need for high-quality training data, interpretability of complex AI algorithms, alignment with safety standards, integration with existing systems, and the lack of interdisciplinary expertise. Addressing these research gaps through further study and collaboration will be crucial to realizing the benefits of AI in this domain, which has long been a critical concern in human and work environments.</p>","PeriodicalId":11686,"journal":{"name":"Environmental Health","volume":"24 1","pages":"32"},"PeriodicalIF":5.3000,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12082979/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental Health","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1186/s12940-025-01186-3","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Introduction: Artificial intelligence (AI) has the potential to significantly enhance workplace safety and mitigate occupational radiation exposure risks by improving the accuracy of assessment and management of these hazards. This study aims to review research on the use of AI in the assessment, monitoring, control, and protection of occupational radiation exposure.
Method: This review was conducted according to the PRISMA guidelines. A comprehensive search was performed in the Web of Science, Scopus, and PubMed databases from inception to April 2024. The search strategy was designed based on the PICO principle and included keywords related to artificial intelligence, occupational exposure, radiation, and industry. The inclusion criteria explored the application of artificial intelligence in the assessment, monitoring, control, and protection against occupational radiation exposure. The quality of the included studies was evaluated using the MMAT critical appraisal tool.
Result: In this review, the initial literature search in the Web of Science, Scopus, and PubMed databases identified 2920 articles. After removing duplicate references, screened based on title, keywords, and abstract, Ultimately, 59 eligible articles were selected, which utilized various artificial intelligence tools, such as expert systems, machine learning, deep learning, and other applied AI models. Of all the articles, 76% had high scores and were considered strong. These studies were categorized into three groups: supervision and assessment, detection and monitoring, protection, control, and personal protective equipment.
Conclusion: The successful application of AI can potentially improve occupational radiation exposure management, but several key challenges must be addressed. These include the need for high-quality training data, interpretability of complex AI algorithms, alignment with safety standards, integration with existing systems, and the lack of interdisciplinary expertise. Addressing these research gaps through further study and collaboration will be crucial to realizing the benefits of AI in this domain, which has long been a critical concern in human and work environments.
导语:人工智能(AI)通过提高评估和管理这些危害的准确性,有可能显著提高工作场所的安全性,并降低职业辐射暴露风险。本研究旨在综述人工智能在职业辐射暴露评估、监测、控制和防护中的应用研究。方法:本综述按照PRISMA指南进行。在Web of Science、Scopus和PubMed数据库中进行了全面的搜索,从开始到2024年4月。搜索策略是基于PICO原则设计的,包括与人工智能、职业暴露、辐射和行业相关的关键词。纳入标准探讨了人工智能在职业辐射照射评估、监测、控制和防护中的应用。使用MMAT关键评估工具对纳入研究的质量进行评估。结果:在本综述中,在Web of Science、Scopus和PubMed数据库中进行了初步文献检索,确定了2920篇文章。在删除重复的参考文献后,根据标题、关键词和摘要进行筛选,最终选择了59篇符合条件的文章,这些文章利用了各种人工智能工具,如专家系统、机器学习、深度学习和其他应用人工智能模型。在所有的文章中,76%的文章得分很高,被认为是强的。这些研究分为三组:监督和评估、检测和监测、保护、控制和个人防护装备。结论:人工智能的成功应用可以潜在地改善职业辐射暴露管理,但必须解决几个关键挑战。这些挑战包括对高质量训练数据的需求、复杂人工智能算法的可解释性、与安全标准的一致性、与现有系统的集成以及缺乏跨学科专业知识。通过进一步的研究和合作来解决这些研究差距对于实现人工智能在这一领域的好处至关重要,这一直是人类和工作环境中一个关键的问题。
期刊介绍:
Environmental Health publishes manuscripts on all aspects of environmental and occupational medicine and related studies in toxicology and epidemiology.
Environmental Health is aimed at scientists and practitioners in all areas of environmental science where human health and well-being are involved, either directly or indirectly. Environmental Health is a public health journal serving the public health community and scientists working on matters of public health interest and importance pertaining to the environment.