ArticleEnvironmental health : a global access science source2025
The role of artificial intelligence in occupational health in radiation exposure: a scoping review of the literature.
Article in Environmental health : a global access science source, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Machine Learning Prediction of Excess Relative Risk for Radiation-Induced Solid Thyroid Cancer Among Nuclear Medicine Healthcare Professionals: A Computational Modeling Study.Bioengineering (Basel, Switzerland) · 2026Article
- Artificial Intelligence and Occupational Health: Global Umbrella Review of Applications and Limitations.Safety and health at work · 2026Review
- Information bubble and skill evolution: a theoretical framework for integrating distributed cognition, cognition offloading, and metacognitive regulation based on AIOE.Frontiers in artificial intelligence · 2026Article
- Application of large language models as decision support tools in occupational health and safety management: a cohort study of industrial workers.Frontiers in public health · 2026Observational
- Occupational Risk of COPD: Insights from a Large Cohort Study.Safety and health at work · 2025Article
- Challenges of using artificial intelligence in Iran's health system: a qualitative study.Journal of preventive medicine and hygiene · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionArtificial 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.
methodThis 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.
resultIn 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.
conclusionThe 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.