Despite sustained efforts to improve construction health and safety (CHS), accident and injury rates remain persistently high, driving increased interest in Artificial Intelligence (AI)-enabled safety solutions. This study presents a thematic systematic literature review of 148 peer-reviewed journal articles published between 2013 and 2025, conducted in accordance with PRISMA guidelines and sourced from Scopus. The synthesis identifies four dominant thematic areas: AI use cases, adoption barriers, realised benefits, and future research directions. Findings indicate a strong concentration of studies on vision-based monitoring, predictive hazard detection, and automated risk assessment, while organisational, ethical, and governance dimensions remain comparatively underexplored. Recurring impediments include data quality limitations, algorithmic opacity, fragmented digital ecosystems, and organisational resistance, highlighting persistent non-technical constraints on implementation. Reported benefits consistently emphasise improved predictive accuracy, real-time situational awareness, and proactive safety intervention, signalling a transition from reactive compliance-based approaches toward anticipatory, data-driven safety management. Based on these patterns, future research should prioritise explainable AI, interoperable data infrastructures, and cross-disciplinary integration to support scalable and trustworthy AI adoption in CHS.
Onososen et al. (Fri,) studied this question.