Inteligencia Artificial Aplicada a la Prevención de Accidentes Laborales en la Industria Manufacturera.

Abstract

Abstract: This research analyzes the application of Artificial Intelligence (AI) as a predictive tool for workplace accidents in the metal-mechanical manufacturing industry, a sector characterized by a high risk of accidents. A qualitative, non-experimental, exploratory design was adopted, based on a literature review and a comparative analysis of four platforms: SafeHS, Enablon, Quentic, and EcoOnline. It is determined that AI significantly contributes to Occupational Health and Safety Management Systems (OHSMS) through real-time monitoring and control with sensors that recognize unsafe conditions; strengthening the preventive culture, reducing reactive rates, and improving business performance. The Enablon and Quentic platforms offer the best advantages due to their high integration capacity, robustness, and flexibility, which guarantees their reliability in high-risk industrial environments. In conclusion, AI is a strategic tool for continuous improvement and transformation of industrial processes and the OHSMS, generating controlled work environments with early warnings, moving from reactive to predictive approaches, improving the capacity for OHS supervision, and reducing the probability of accidents or critical incidents.