Deploying artificial intelligence for occupational and environmental health in waste management: a systematic review

Abstract

Objectives This systematic review examines how artificial intelligence (AI) mitigates occupational and environmental health risks in waste management, a sector where workers and communities face infectious, toxic, and physical hazards. Methods Following PRISMA guidelines, we systematically searched the SCOPUS database and reviewed 81 peer-reviewed articles. The analysis focused on the types of AI technologies applied, their deployment across waste management stages, and the specific health risks they address. Results The analysis identifies five types of health risks targeted by AI applications. AI is deployed across five waste management stages and four technical paradigms (supervised, unsupervised, reinforcement, and deep learning). AI alleviates hazards via three primary pathways: automated risk-source isolation, robotic substitution for high-risk tasks, and macro-level exposure monitoring. Conclusion Current public health research has largely neglected health risk reduction gains from waste management technological innovation. Institutional innovations that can facilitate responsible AI deployment are discussed. This interdisciplinary perspective underscores the necessity of cross-sector collaboration to tackle public health issues.