Keyword: Exposome

ABSTRACT: Environmental carcinogens remain significant contributors to cancer development; however, conventional risk assessment has largely focused on population-level exposure without adequately accounting for variation in individual susceptibility. Individuals exposed to comparable environmental hazards can nevertheless face different cancer risks, as susceptibility is influenced by genetic and epigenetic variation, metabolic capacity, immune function, age, lifestyle, comorbidities, and cumulative exposure histories. The growing availability of environmental, clinical, molecular, and longitudinal health data offers new opportunities to characterize these differences with greater precision. In this context, artificial intelligence (AI), particularly machine learning and other multimodal analytical approaches, can identify complex patterns across heterogeneous datasets that may remain difficult to detect through conventional statistical methods. This narrative review explores the potential of AI to enable more precise assessment of individual susceptibility to environmental carcinogens and proposes a framework that brings together exposure profiles, host susceptibility, molecular responses, lifestyle and contextual factors, and longitudinal information. Within this framework, AI serves as an analytical layer for generating individualized risk profiles and informing prevention strategies, including exposure reduction, risk-informed surveillance, and targeted preventive interventions where clinically appropriate. However, several challenges may limit its translation into practice, including weaknesses in exposure assessment, data quality, algorithmic bias, limited interpretability, privacy concerns, uncertainty surrounding causal inference, insufficient external validation, and implementation barriers. Accordingly, AI should complement, not replace, established epidemiological and mechanistic approaches. With rigorous prospective validation, transparent model development, and appropriate ethical and clinical safeguards, AI-enabled susceptibility assessment may support a transition from predominantly population-based approaches to more individualized and risk-informed environmental cancer prevention.