Applications of artificial intelligence in occupational noise assessment and noise‑related health outcomes: a scoping review

Occupational noise remains one of the most prevalent workplace hazards worldwide and is associated with a wide range of adverse health effects, particularly noise‑induced hearing loss (NIHL). Recently, artificial intelligence (AI) has been increasingly applied in occupational noise research; however, the scope and focus of these approaches have not been systematically mapped. This review aims to provide an overview of how AI has been used in occupational noise assessment and noise‑related health outcomes. Articles were identified through systematic database searching and screened using predefined inclusion criteria. Eligible studies were charted and synthesized descriptively, with AI applications categorized according to their primary research objectives. A total of 37 studies were included. Most studies focused on noise‑related health outcomes (n = 30), while fewer addressed noise exposure assessment and prediction (n = 7). Within health‑related applications, auditory outcomes were the predominant focus, followed by non-auditory and combined outcomes. AI was primarily applied for health outcome prediction and risk stratification, whereas comparatively limited attention was directed toward preventive applications such as exposure monitoring and noise control. By mapping current research patterns, this review highlights knowledge gaps and underscores the need for future research integrating exposure assessment, health outcomes, and preventive decision‑support within occupational noise management.