Advancing Occupational Noise-Induced Hearing Loss Risk Assessment in the Era of Large-Scale Data

Occupational noise-induced hearing loss (NIHL) remains a leading occupational disorder worldwide and continues to impose substantial burdens on workers, employers, compensation systems, and health systems. ISO 1999 remains the principal framework for estimating population-level hearing loss associated with occupational noise exposure, while ISO 9612 provides the corresponding methodology for exposure determination. However, the context in which these models are applied has changed substantially. Hearing conservation programs, surveillance systems, and retrospective industrial cohorts now generate large audiometric and exposure datasets that allow direct comparisons between predicted and observed outcomes in real worker populations. This narrative review examines how such data can strengthen occupational NIHL risk assessment through validation, characterization of prediction gaps, population-specific calibration, harmonized reporting of early high-frequency indicators, and prevention-oriented interpretation. It also considers the continued value of energy-based approaches and the limits of L Aeq -only modeling in complex-noise environments characterized by impulsiveness, intermittency, or non-Gaussian temporal structure. The argument is not that established standards should be discarded, but that their implementation should become more transparent, data-informed, and adaptive. A staged pathway is proposed in which established frameworks are retained while their use is strengthened through structured validation procedures, calibration strategies, explicit applicability statements, and data infrastructures that support future model refinement. The proposed framework also treats real-world data heterogeneity, healthy-worker selection, co-exposures, baseline-rule inconsistency, and the field operability of complex-noise metrics as explicit determinants of model transportability. For prediction applications, it emphasizes prespecified endpoints, transparent calibration, external validation, and avoidance of overinterpretation by machine learning or other high-dimensional methods.