POSSIBILITIES OF USING ARTIFICIAL INTELLIGENCE FOR OCCUPATIONAL SAFETY USING THE EXAMPLE OF MEDICAL EXAMINATIONS ORGANIZATION
The article explores the potential applications of artificial intelligence (AI) to optimize occupational safety processes and minimize the risk of human errors, particularly in organizing periodic medical examinations of employees. The aim of the study was to assess the effectiveness and practical relevance of AI in reducing anthropogenic errors through the automation of tasks related to professional risk analysis and compliance with regulatory requirements.As part of the research, an experiment was conducted using AI algorithms, exemplified by the ChatGPT system, designed to process information and generate lists of employees requiring medical examinations. Data from staffing schedules, job hazard assessment records, and professional risk evaluation results were utilized. A comparison of the AI-generated lists with the provided documentation demonstrated full compliance with regulatory standards.The results confirm that AI implementation enhances data processing accuracy, reduces labor costs, and minimizes the likelihood of errors. Key risks associated with AI adoption were also identified, including dependence on input data quality, ensuring information confidentiality, and addressing employee skepticism.The practical significance of the study lies in the potential integration of AI technologies into occupational safety systems to improve efficiency and ensure workers’ safety. The authors highlight prospects for further research aimed at adapting AI to industry-specific conditions and developing implementation methodologies.The article underscores the relevance and necessity of employing modern digital technologies to address occupational safety challenges, including enhanced automation, precision, and compliance with legal requirements.
Authors
- А.В. Федосов (ORCID: https://orcid.org/0000-0002-7853-1800)
- Robert R. Musin
- Denis S. Bannykh
- Naida A. Baigeldieva
Publication Details
- Journal
- Oil and Gas Business
- Published
- 2025-04-22
- DOI
- https://doi.org/10.17122/ogbus-2025-2-97-121
- Primary Topic
- Healthcare Systems and Public Health
- Type
- article
- Field-Weighted Citation Impact
- 0.00