Xuexing Wang, Xingxing Tang, Chunmei Wei, Jinsong Xu, Kai Sun, Guozhong Zhou
The profound heterogeneity of cancer pain renders conventional "one-size-fits-all" analgesic strategies ineffective, creating an urgent need for tools that can accurately subtype pain and predict individual therapeutic responses. This review proposes a transformative solution: a patient-specific "digital twin" for pain, powered by multimodal Artificial Intelligence (AI). This framework leverages advanced AI methodologies, including large foundation models and transformer architectures, to integrate and interpret these complex datasets. By synthesizing diverse data streams-from genomics and digital pathology to clinical text and patient-reported outcomes-the digital twin can uncover complex, non-linear patterns to simultaneously classify pain subtypes (eg, nociceptive, neuropathic, nociplastic) and predict sensitivity to specific analgesic regimens. While promising, we critically assess major translational barriers, including data scarcity, model interpretability, the need for robust prospective validation, and privacy concerns. To bridge the gap from concept to clinic, we outline a concrete research roadmap and a "Hybrid Telemedicine and On-Site Expert" implementation model. This AI-driven framework offers a path toward precise, dynamic, and truly personalized cancer pain management, aiming to tangibly improve outcomes for patients worldwide.