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The role of artificial intelligence in occupational health in radiation exposure: a scoping review of the literature

Environmental Health volume 24, Article number: 32 (2025) Cite this article

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.

Peer Review reports

Introduction

Artificial intelligence (AI) is characterized by a system that can carry out cognitive tasks associated with human intelligence, such as learning and reasoning, often as well as or more proficiently than humans [1]. AI is able to considerably enhance safety monitoring and protocols across multiple industries by examining extensive empirical datasets, discerning patterns, and predicting potential hazards, which will enhance workplace safety [2]. With the advancement of AI technologies, there are opportunities to improve accuracy [3]. One of the risks in the workplace is exposure to radiation [4]. Ray or radiation is energy that spreads in the form of waves or particles in a vacuum or in a material environment. Some rays have mass, and some do not, and depending on the amount of energy, they have the power to penetrate matter [5]. The purpose of protection against radiation is to ensure that the amount absorbed by each person (except patients) is not more than the maximum allowed amount or that the minimum exposure is possible and justified [6]. The physical effects of ionizing radiation range from minor and temporary disorders in some physiological actions to serious risks such as reduced lifespan decreased immunity to diseases, reproductive issues, cataracts, blood cancer, other types of cancer, and damage to a developing fetus [4]. Occupational radiation exposure is a significant concern in various industries, including healthcare, nuclear power, and manufacturing [7, 8]. Understanding the potential risks and effects of occupational radiation exposure is crucial for protecting workers and ensuring workplace safety. This exposure can occur in industries such as healthcare [9,10,11]. Taking a proactive approach not only enhances worker safety but also ensures compliance with regulatory standards and reduces the likelihood of costly incidents. As AI technology continues to advance, we can expect more sophisticated solutions to further enhance radiation safety in the workplace.

A 2009 review study by Wakeford, aims to investigate the risks associated with occupational exposure to ionizing radiation, focusing on the health impacts of long-term, low-level radiation exposure. It aims to complement existing epidemiological data by examining groups such as healthcare workers, miners, and Chernobyl clean-up personnel while also evaluating the health risks from internally deposited radioactive materials, particularly alpha-particle emitters. Additionally, the study explores potential non-cancer health effects, such as heart disease and cataracts, and highlights the importance of international collaboration in deriving reliable risk estimates and establishing guidelines for radiological protection. Ultimately, this research seeks to understand better the health effects of ionizing radiation in various occupational settings [12].

Another review study conducted by Pillai et al. in 2019,,"Using Artificial Intelligence to Improve the Quality and Safety of Radiation Therapy,"examines the impact of machine learning (ML) on radiation therapy, focusing on how it can enhance treatment quality and patient safety. It discusses the shift from generalized to personalized treatment approaches, explores various applications of ML, such as outcome prediction and clinical decision support, and identifies challenges in algorithm development and integration with existing practices. Additionally, the study suggests future research directions and collaboration strategies to address these challenges and further improve the quality and safety of radiation therapy [13]. In a 2021 review, Muhammad Ikmal Ahmad et al. examined the key role of the Internet and drones in optimizing radiation monitoring systems and enhancing safety and also pointed out existing challenges and new research opportunities in this area. With this review, they sought to create a more comprehensive understanding of the applications of new technologies in radiation monitoring and security in the nuclear industry [14].

Due to the wide application of AI technology in various fields of monitoring and investigation and radiation protection and monitoring, this study was conducted as a scoping review. The purpose of this study is to review studies that discuss the use of artificial intelligence in the assessment, monitoring, control, and protection of occupational radiation exposure. These studies highlight the potential of artificial intelligence in enhancing radiation protection measures.

Materials and methods

Search strategy and study selection

This research was conducted following PRISMA guidelines. A systematic review was carried out with a comprehensive search strategy for articles without time restrictions. It includes articles from the beginning to April 2024 from Web of Science, Scopus, and PubMed Databases. The articles were searched and reviewed twice and by three researchers separately.

The search utilized the following keywords and the search strategy based on the PICO principle:

  • ((TS = (artificial intelligence)) OR TS = (machine learning)) OR TS = (deep learning) AND (((((((((TS = (exposure)) OR TS = (assessment)) OR TS = (control)) OR TS = (monitoring)) OR TS = (effect)) OR TS = (evaluation)) OR TS = (protection)) OR TS = (prevention)) OR TS = (prediction)) OR TS = (Safety) AND ((((TS = (industry)) OR TS = (industrial)) OR TS = (occupation*)) OR TS = (work)) OR TS = (workplace) AND (TS = (radiation)) OR TS = (*ray) NOT ((((((TS = (patient*)) OR TS = (Prescribe*)) OR TS = (animal*)) OR TS = (drug)) OR TS = (crime)) OR TS = (child*)) OR TS = (COVID)

Inclusion and exclusion criteria

The included studies consisted of case–control studies, cohort studies and descriptive studies that explored the application of artificial intelligence in assesment, monitoring, control and protection of occupational exposure to radiation.

Data extraction

Articles were imported into the Endnote software, duplicate studies were removed, and then screened according to the selection criteria. Data extraction included the following information: first author (year), AI tools used, purpose of the study and outcome.

Assessing the quality of articles

In this study, MMAT,Footnote 1 a critical appraisal tool, was used to assess the quality of the articles. The MMAT was developed in 2006 and was revised in 2011. The present version, 2018, was developed on the basis of findings from a literature review of critical appraisal tools, interviews with MMAT users, and an e-Delphi study with international experts. The MMAT developers are continuously seeking improvement and testing of this tool. Users’ feedback is always appreciated. This document comprises a checklist (Part I) and an explanation of the criteria (Part II). In Part I, the checklist includes methodological quality criteria, which are listed in 5 categories of study designs. In Part II of this document, indicators are added for some criteria [15]. Three answers determine the scoring method for the questions in this tool:"Yes"with a score of"1","No"with a score of"0", and"Not relevant"without a score. The final score is calculated as a fraction of the sum of the 1 points over the total 0 and 1 answers. A score below 0.5 is considered weak, between 0.5 and 0.75 is considered moderate, and above 0.75 is considered strong.

Results and discussion

Search results and study selection

Two thousand nine hundred twenty articles were identified in the initial literature review using Web of Science, Scopus, and PubMed databases. After removing duplicate references, 2572 articles underwent screening based on their titles, keywords, and abstracts. Of these, 2483 articles were excluded for not meeting the inclusion criteria. The exclusion criteria were as follows: 1) the review studies. 2) Studies involving children and inhumane populations, such as animals. 3) Studies involving prescribing and taking medicine. Considering these criteria, the number of articles reached 89. Then, by reading the full text of the articles and considering the criteria for selecting studies that examine job/work/industry as one of the studied variables, the number of articles reached 59. Of all the articles, 76% had high scores and were considered strong; therefore, these studies were selected for eligibility and quality assessment, as shown in Fig. 1. The Flowchart illustrates the literature review process, detailing the identification, screening, and selection of studies that examined occupation/work/industry variables, ultimately resulting in 59 eligible articles. These studies’ applied intelligence tools include expert systems and machine learning, deep learning, and other applied artificial intelligence models, as shown in Fig. 2. The applied intelligence tools in this study include expert systems, machine learning, deep learning, and other artificial intelligence models. Expert systems act as a model for decision-making and solving complex problems based on expert knowledge. Meanwhile, machine learning is a powerful approach in AI that allows models to learn from data and make accurate predictions without manual programming. In this context, deep learning, as a subset of machine learning, utilizes multi-layer neural networks to analyze complex data such as images and sounds. This combination of tools enables intelligent systems to perform effectively in various fields, including healthcare, business, and data analysis. In this study, according to the role of artificial intelligence in occupational health in exposure to radiation, the articles are grouped into three groups: articles related to supervision and Assessment, articles related to Detection and Monitoring, and articles related to Protection, Control, and PPE. Meanwhile, 74.14% of the studies corresponded to strong quality, 24.14% moderate quality, and 1.7% weak quality.

Fig. 1
figure 1

Flowchart of the study selection process, following PRISMA-ScR guidelines

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Fig. 2
figure 2

Categorization of AI tools used in the studies included in this review. *SWIPT: Simultaneous Wireless Information and Power Transfer; IOT: Internet of Things; K-N–N: K-Nearest Neighbors; Mont Carlo: a computational technique that uses random sampling to obtain numerical results; An Export System refers to systems that can export data, models, or outputs generated by AI applications, Mix Method involves the use of mixed methods in machine learning and AI, combining quantitative and qualitative data; DL: Deep Learning; ANN: Artificial Neural Network; ML: Machine Learning

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Figure 1 illustrates the flowchart of reviewed articles and the selection process in this review, and 2 illustrates AI tools used in studies broken down by percentage.

Application of artificial intelligence in supervision and assessment

Among the articles reviewed, 16 are related to monitoring and evaluation, which are listed in Table 1. The topics covered in these studies included the following: assisting inspections regarding radiation protection, dosimetry systems and estimation of personal equivalent dose, reviewing the effect of the mentioned absorbed dose, developing software systems to keep the dose within acceptable limits, providing an intelligent system equipped with the Internet of Things (IoT) for radiation monitoring and alerting, dose rate mapping, and radiation transfer models in nuclear power plants. Overall, these studies focus on identifying sources and predicting radiation with an emphasis on worker safety.

Table 1 Articles related to monitoring and assessment
Full size table

Application of artificial intelligence in detection and monitoring

Among the articles reviewed, 32 are related to detection and monitoring, which are listed in Table 2. The topics covered in these studies include: developing an intelligent radiation detector system for remote radiation monitoring, predicting radiation properties and the annual dose of workers, identifying representative locations among environmental variables, forecasting indoor solar radiation, tracking radioactive particles, optimizing the number of detectors, creating a wireless sensor network, designing detection systems to prevent'blind spots'in monitoring, accurately simulating detectors, radiation dose prediction, detecting ionizing radiation particles using detector responses, identifying abnormal radioactive signatures with detectors, evaluating detector performance, and locating and identifying radioactive sensitive spots, as well as estimating medical exposure control measures.

Table 2 Articles related to detection and monitoring
Full size table

Application of artificial intelligence in protection, control and PPE

Among the studied articles, ten are related to detection and monitoring, which are mentioned in Table 3. In these studies, radiation control, the development of an expert system for the transfer of radioactive materials, a model for extracting the properties of materials for radiation protection, the factors of creating an equivalent environmental dose for the construction of protective concrete, and approaches to ensure the correct use of PPE are mentioned in these studies, excellence in preparing instantaneous control schemes, introduction of advanced technologies for glove boxes, protection against electromagnetic radiation, phased approach strategy towards radiation control, high-performance electromagnetic response mechanism of flexible absorbers.

Table 3 Articles related to protection, control, and PPE
Full size table

Conclusion

Artificial intelligence (AI) has emerged as a leading in recent decades technology across various scientific and industrial fields. One area that has this technology has significantly impacted is radiation protection. A recent article by Sylvain Andresz and colleagues effectively explores the capabilities of AI and machine learning (ML) in enhancing strategies for protecting individuals and the environment from radiation exposure. Their research findings indicate that the utilization of AI can substantially improve the accuracy of radiation exposure assessments and the efficiency of risk management systems. Additionally, the authors emphasize the importance of collaboration between radiation protection professionals and data scientists, asserting that such partnerships can lead to more effective algorithm development and optimized scientific and technological outcomes. This paper will examine the applications of AI in radiation protection, along with the challenges and opportunities that arise in this field [74].

The applications of artificial intelligence (AI) are likely to enhance the quality of assessment, monitoring, control, and protection against occupational radiation exposure. AI thus holds the potential to improve protective measures against radiation. Radiation protection has been a concern for both humans and workplaces for decades. Radiation monitoring plays a critical role in preventive measures, benefiting industries, hospitals, and any activity involving radioactive materials. Beyond occupational settings, radiation monitoring is also essential for responding to emergencies. Based on the studies reviewed in this scoping review, significant research gaps exist in this field, including: 1) The need for sufficient and high-quality data. Many AI algorithms require comprehensive and high-quality training datasets, which are limited in some areas. Collecting and developing datasets related to real-world radiation exposure presents a significant challenge. 2) Interpretation and transparency of algorithms. Some AI models are complex and require greater explanation and interpretability. This presents a challenge in developing reliable and interpretable algorithms specifically for radiation applications. 3) Compliance with standards and regulations. The use of AI must align with and be validated against existing safety standards and regulations. This highlights the need to develop legal and ethical frameworks for applying AI in this domain. 4) Integration with existing systems. Incorporating AI into current monitoring and evaluation systems poses challenges and requires the development of technological infrastructures to facilitate the efficient use of AI. 5) Specialized and interdisciplinary skills, as well as more experts, are needed to become familiar with artificial intelligence and the science of radiation protection.

In a 2021 review, Gomez-Fernandez et al. examined the applications of machine learning in the nuclear industry and its potential to improve nuclear safety and radiation detection. They analyzed learning networks to determine whether domain-related features were identified. They concluded that a human-centric approach could help increase transparency and trust in the decisions of deep learning algorithms [75].

These research gaps indicate that further studies and interdisciplinary collaborations are still required. As observed in the review studies, each dealt with a part of evaluation, protection, and control. Our study’s advantage is that we considered all aspects and referred to studies that had been conducted in these fields.

Data availability

The present study is a review, and the data are publicly available in various databases.

Notes

  1. Mixed methods appraisal tool.

Abbreviations

AI:

Artificial intelligence

PRISMA:

Preferred Reporting Items for Systematic reviews and Meta-Analyses

PICO:

Patient/Problem, Intervention, Comparison and Outcome

MMAT:

Mixed methods appraisal tool

PPE:

Personal protection equipment

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Acknowledgements

The authors express their gratitude to Shiraz University of Medical Sciences for providing access to the required databases to extract articles and data necessary for this research. Special thanks are also extended to Anahita Fakharpour for her assistance in extracting data from certain articles.

Funding

The authors did not receive funding to conduct this study.

Author information

Authors and Affiliations

  1. Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran

    Zohreh Fazli

  2. Department of Occupational Health, School of Public Health and Safety, Bam University of Medical Sciences, Bam, Iran

    Zohreh Fazli

  3. Iranian Airport Company, Bam, Iran

    Mehran Sadeghi

  4. Non-Communicable Diseases Research Center, Shiraz University of Medical Sciences, Shiraz, Iran

    Mohebat Vali

  5. Department Of Occupational Health and Safety Engineering, School of Health, Shiraz University of Medical Sciences, Shiraz, Iran

    Parvin Ahmadinejad

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  1. Zohreh Fazli
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PA, ZF, and MS participated in the project’s design. ZF and PA were responsible for searching, studying, and reviewing articles, as well as extracting data from them. ZF and PA prepared the initial draft of the article. PA, ZF, and MV assessed the quality of the articles used in the study. PA was in charge of translating, editing, revising, reviewing, and overseeing the article throughout all stages of the research. All authors have approved the final version.

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Correspondence to Parvin Ahmadinejad.

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Fazli, Z., Sadeghi, M., Vali, M. et al. The role of artificial intelligence in occupational health in radiation exposure: a scoping review of the literature. Environ Health 24, 32 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12940-025-01186-3

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12940-025-01186-3

Keywords

Environmental Health

ISSN: 1476-069X