Decoding chronic pain: integrating genetics, neuroimaging, and AI for precision management

Chronic pain is increasingly recognised as a standalone medical condition shaped by interacting biological, psychological, and social determinants, affecting nearly one in three adults worldwide. This review synthesises contemporary evidence on the epidemiology, mechanisms, and management of chronic pain, with emphasis on the convergence of genetic, neurobiological, and psychosocial factors. We draw on recent population-based studies, clinical trials, neuroimaging research, and multi-omic genetic analyses to highlight the complexity and heterogeneity of this condition. Chronic pain disproportionately affects older adults, women, and socioeconomically disadvantaged groups, and frequently co-occurs with psychiatric, cardiovascular, and neurodegenerative disorders, reflecting shared pathways of maladaptive neuroplasticity. Although pharmacological therapies often provide modest long-term benefit, integrated psychological, physiotherapeutic, and interventional approaches demonstrate more sustainable improvements in function and quality of life. Advances in genomics and large-scale genome-wide association studies (GWAS) have revealed extensive polygenic overlap with psychiatric and immune traits, while neuroimaging consistently demonstrates alterations within prefrontal, insular, and limbic circuits that shape pain perception and persistence. Despite reliance on subjective symptom reporting, emerging digital phenotyping, wearables, and AI tools offer promising avenues for objective monitoring and personalised treatment. Integrating biological, behavioural, and environmental data will be essential to achieving truly precision-based chronic pain care.