Risk and protective factors in addictive behaviors: Integrating artificial intelligence and traditional analytical methods to assess social and psychological determinants

PURPOSE OF REVIEW: Addictive behaviors, including both substance use disorders and behavioral addictions, arise from complex interactions among biological, psychological, social, and environmental factors including digital ones. This review focuses on the assessment of social and psychological risk and protective factors, highlighting how artificial intelligence and machine learning approaches complement conventional qualitative and quantitative methodologies. The aim is to clarify how these tools can enhance understanding, prediction, and prevention of addictive behaviors. RECENT FINDINGS: Recent research identifies impulsivity, emotion dysregulation, peer norms, and family functioning as central psychosocial risk factors for addictive behaviors. Protective factors - such as self-efficacy, social support, and family cohesion - moderate these risks. Conventional analyses provide foundational evidence, while ML methods (predictive machine learning, explainable artificial intelligence, reinforcement learning) now enable integration of multimodal data, detection of nonlinear patterns, and identification of latent psychosocial profiles. Emerging studies demonstrate potential for early-warning prediction and personalized intervention design. SUMMARY: AI/ML offers unprecedented opportunities to advance addiction science by handling high-dimensional psychosocial and behavioral data. Yet, ethical, interpretative, and causal challenges persist. The most promising path forward lies in synergizing theory-driven analytics with data-driven AI approaches to achieve more precise and contextually grounded prevention and intervention strategies for addictive behaviors.