El aprendizaje profundo en seguridad y salud laboral y su contribución a la gestión de riesgos en el lugar de trabajo: una revisión sistemática
| Pages | 1-8 |
| Date | 05 July 2026 |
| Published date | 05 July 2026 |
| Author | Milner Segovia Hermoza,Katherin Lizet Huamani Morales,Milner Segovia Segovia |
| Subject Matter | Ciencias sociales |
Deep learning in occupational safety and health
and its contribution to workplace risk management:
A systematic review
El aprendizaje profundo en seguridad y salud laboral y su contribución a la
gestión de riesgos en el lugar de trabajo: una revisión sistemática
Recibido: 18/05/2025 - Aceptado: 26/09/2025
Milner Segovia Hermoza
https://orcid.org/0000-0002-3398-5163
milner.segovi.h@uni.edu.pe
Universidad Nacional de Ingeniería. Lima, Perú
Katherin Lizet Huamani Morales
https://orcid.org/0000-0001-6917-8614
katherin.huamani@unmsm.edu.pe
Universidad Nacional Mayor de San Marcos. Lima, Perú
Milner Segovia Segovia
https://orcid.org/0000-0002-2281-748X
milner.segovia@unsaac.edu.pe
Universidad Nacional San Antonio Abad del Cusco. Cusco, Perú
Abstract
Occupational health and safety (OHS) is essential in all industrial sectors due to the significant impact of accidents
and occupational illnesses. Traditionally, risk management has relied on periodic assessments and reactive control
measures; however, artificial intelligence and deep learning have enabled the development of more proactive and
efficient approaches. These technologies facilitate the analysis of large volumes of data to identify hidden patterns,
predict risks, and improve workplace safety. In th is context, this s tudy analyzes the c urrent use of d eep learning
in OHS, focusing on its implementation, perceived effectiveness, and the challenges it presents. Furthermore, it
examines its application in sectors such as construction, logistics, and mining, where it contributes to risk
prevention and the detection of unsafe behaviors. To this end, a systematic review was conducted following the
PRISMA protocol, including 14 open-access articles selected from an initial pool of 274 publications retrieved from
Scopus, PubMed, Web of Science, and DOAJ. The analysis concludes that deep learning has great potential to
reduce unsafe behaviors by identifying and detecting key variables, such as information management, that
influence the occurrence of incidents and hazardous conditions. Therefore, this technology emerges as a valuable
tool to support OHS professionals in prevention, control, and decision-m aking in various work environments.
Keywords: deep learning, occupational risks, workplace safety.
Resumen
La salud y seguridad en el trabajo (SST) es fundamental en todos los sectores industriales debido al impacto
significativo de los accidentes y las enfermedades laborales. Tradicionalmente, la gestión de riesgos se ha basado
en evaluaciones periódicas y medidas de control re activas; sin embargo, la intelig encia artificial y el aprendi zaje
profundo han permitido desarrollar enfoques más preventivos y eficientes. Estas tecnologías facilitan el análisis
de grandes volúmenes de datos para identificar patrones ocultos, predecir riesgos y mejorar la seguridad laboral.
En este marco, el presente estudio analiza el uso actual del aprendizaje profundo en SST, centrándose en su
implementación, la percepción de su eficacia y los desafíos que plantea. Además, se examina su aplicación en
sectores como la construcción, la logística y la minería, donde contribuye a la prevención de riesgos y a la
detección de comportamientos inseguros. Para ello, se llevó a cabo una revisión sistemática siguiendo el
protocolo PRISMA, que incluyó 14 artículos de acceso abierto seleccionados de un total inicial de 274
publicaciones recuperadas de Scopus, PubMed, Web of Science y DOAJ. A partir del análisis realizado, se
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