REDECA: A Novel Framework to Review Artificial Intelligence and Its Applications in Occupational Safety and Health

Details

• Personal Author:
  Darabi H ; Issa SF ; Pishgar M ; Pratap P ; Sietsema M
• Description:
  Introduction: The field of artificial intelligence (AI) is rapidly expanding, with many applications seen routinely in health care, industry, and education, and increasingly in workplaces. Although there is growing evidence of applications of AI in workplaces across all industries to simplify and/or automate tasks there is a limited understanding of the role that AI contributes in addressing occupational safety and health (OSH) concerns. Methods: This paper introduces a new framework called Risk Evolution, Detection, Evaluation, and Control of Accidents (REDECA) that highlights the role that AI plays in the anticipation and control of exposure risks in a worker's immediate environment. Two hundred and sixty AI papers across five sectors (oil and gas, mining, transportation, construction, and agriculture) were reviewed using the REDECA framework to highlight current applications and gaps in OSH and AI fields. Results: The REDECA framework highlighted the unique attributes and research focus of each of the five industrial sectors. The majority of evidence of AI in OSH research within the oil/gas and transportation sectors focused on the development of sensors to detect hazardous situations. In construction the focus was on the use of sensors to detect incidents. The research in the agriculture sector focused on sensors and actuators that removed workers from hazardous conditions. Application of the REDECA framework highlighted AI/OSH strengths and opportunities in various industries and potential areas for collaboration. Conclusions: As AI applications across industries continue to increase, further exploration of the benefits and challenges of AI applications in OSH is needed to optimally protect worker health, safety and well-being. [Description provided by NIOSH]
• Subjects:
  Accidents, Occupational Agriculture Construction Industry Education Environmental Exposure Manufacturing And Industrial Facilities Mining Occupational Health Oil And Gas Industry Risk Assessment Risk Factors Safety Transportation Workplace

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• Keywords:
  Accidents Agricultural Industry Artificial Intelligence Author Keywords: Artificial Intelligence Construction Industry Exposure Levels Future Of Work Gas Industry Hazards Health Care Industrial Environment Machine Learning Algorithms Mining Industry Occupational Safety And Health Oil Industry Protective Measures Risk Factors Robotic Devices Sensor Devices Sensors Transportation Industry Work Environment Worker Health Worker Health And Safety Workers Worker Safety

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• ISSN:
  1660-4601
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  Illinois ; OSHA Region 5
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  18
• Issue:
  13
• NIOSHTIC Number:
  nn:20063197
• Citation:
  Int J Environ Res Public Health 2021 Jul; 18(13):6705
• Contact Point Address:
  Houshang Darabi, Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, IL 60609
• Email:
  hdarabi@uic.edu
• Federal Fiscal Year:
  2021
• Performing Organization:
  University of Illinois at Chicago
• Peer Reviewed:
  True
• Start Date:
  20050701
• Source Full Name:
  International Journal of Environmental Research and Public Health
• End Date:
  20290630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:8d9aa6f060da13378893bf55a262b66d1667f35f1388b9be7e19aefaa4d7b0e67f872a20ac0db1d92f5f211be0ac3f88c8870c0faa551a9d1a24387d888e2281
• Download URL:
  https://stacks.cdc.gov/view/cdc/226301/cdc_226301_DS1.pdf
• File Type:
  [PDF - 2.33 MB ]