Digital Pain Medicine in the Era of Precision Healthcare: Integrating Artificial Intelligence, Wearable Biosensors, and Predictive Analytics for Personalized Pain Management

Abstract

Digital pain medicine is taking shape as a precision health care model to help in pain management. This comprehensive review examines the integration of artificial intelligence, wearable biosensors, digital phenotyping, predictive analytics, and personalized therapeutic strategies in modern pain management. Current evidence suggests that wearable technologies and remote monitoring systems can capture continuous physiological and behavioral data, offering a more objective view of pain beyond episodic clinical encounters and self-reported scales. Artificial intelligence and machine learning can transform multimodal data into clinically useful insights for pain detection, classification, risk stratification, treatment planning, and decision support. Predictive analytics, pharmacogenomics, biomarkers, and digital twin technologies further support individualized care pathways across chronic, neuropathic, cancer-related, postoperative, pediatric, and telehealth-enabled pain conditions. Yet, implementation remains hampered by challenges related to data quality, interoperability, privacy, governance, algorithmic transparency, clinical validation, regulatory oversight, and equitable access. Future progress will require rigors validated, explainable, secure, clinician-guided digital systems that augment, rather than replace, clinical judgement. Digital pain medicine offers a path to more proactive, objective and patient-centric pain care, but its success will depend on responsible translation into real-world clinical practice.