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Advancements in Artificial Intelligence and Machine Learning for Occupational Risk Prevention: A Systematic Review on

Pablo Armenteros-Cosme1, Marcos Arias-González1, Sergio Alonso-Rollán1

  • 1BISITE Research Group, University of Salamanca, C. Espejo, 2, 37007 Salamanca, Spain.

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PubMed
Summary

Artificial intelligence (AI) and machine learning (ML) are increasingly used for occupational risk prevention. This review found AI excels in hazard detection using visual data but needs multimodal data and interpretable models for better safety.

Keywords:
artificial intelligencemachine learningoccupational safetyrisk preventionsystematic literature mapping

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Area of Science:

  • Occupational Health and Safety
  • Artificial Intelligence in Industry
  • Predictive Analytics for Workplace Hazards

Background:

  • Occupational risk prevention is crucial for workplace safety, with AI and ML emerging as key tools.
  • The study addresses the growing application of AI for predicting and preventing workplace hazards and occupational diseases.
  • Existing literature on AI for hazard detection and risk prediction is systematically reviewed.

Purpose of the Study:

  • To identify, evaluate, and synthesize literature on AI algorithms for detecting and predicting hazardous environments and occupational risks.
  • To focus on predictive modeling and prevention strategies within occupational safety.
  • To assess the current state and limitations of AI applications in workplace hazard detection.

Main Methods:

  • Systematic literature review following PRISMA 2020 protocol, including conference proceedings and technical reports.
  • Searches conducted in IEEE Digital Library, PubMed, Scopus, and Web of Science for English articles published from 2019 onwards.
  • Rigorous screening and quality assessment of 61 selected articles, excluding systematic reviews and low-quality studies.

Main Results:

  • Significant growth in publications on AI for occupational safety from 2021-2024, with major contributions from China, South Korea, and India.
  • Primary applications identified in high-risk sectors like construction, mining, and manufacturing.
  • Deep learning models, especially CNNs and YOLO, analyzing visual data from cameras (over 40% of studies) are the predominant approach.

Conclusions:

  • Current AI systems for hazard detection are limited by over-reliance on visual data and lack of standardization.
  • Future research should integrate multimodal data (visual, environmental, physiological) and develop interpretable AI (XAI) for enhanced accuracy and trust.
  • Addressing societal implications like privacy and worker displacement requires transparent data policies and regulatory frameworks.