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已发表论文
以多模态人工智能重塑精准癌痛管理：亚型划分和治疗反应预测综述
Authors Wang X , Tang X, Wei C, Xu J, Sun K , Zhou G
Received 26 September 2025
Accepted for publication 24 January 2026
Published 2 February 2026 Volume 2026:19 570481
DOI https://doi.org/10.2147/JPR.S570481
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Amitabh Gulati
Xuexing Wang,1– 3,* Xingxing Tang,4 Chunmei Wei,1 Jinsong Xu,2 Kai Sun,3,* Guozhong Zhou2,*
1Department of Oncology, Anning First People’s Hospital Affiliated to Kunming University of Science and Technology, Anning, People’s Republic of China; 2Pain Medicine Department, Anning First People’s Hospital Affiliated to Kunming University of Science and Technology, Anning, People’s Republic of China; 3Department of Oncology, Ganzhou Cancer Hospital, Ganzhou, People’s Republic of China; 4Cadre Medical Department, Third Affiliated Hospital of Kunming Medical University, Kunming, 650000, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Guozhong Zhou, Email 20220247@kust.edu.cn Kai Sun, Email sunkai0927@foxmail.com
Abstract: 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.
Keywords: cancer pain, multimodal artificial intelligence, digital twin, pain subtyping, individualized treatment prediction
1Department of Oncology, Anning First People’s Hospital Affiliated to Kunming University of Science and Technology, Anning, People’s Republic of China; 2Pain Medicine Department, Anning First People’s Hospital Affiliated to Kunming University of Science and Technology, Anning, People’s Republic of China; 3Department of Oncology, Ganzhou Cancer Hospital, Ganzhou, People’s Republic of China; 4Cadre Medical Department, Third Affiliated Hospital of Kunming Medical University, Kunming, 650000, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Guozhong Zhou, Email 20220247@kust.edu.cn Kai Sun, Email sunkai0927@foxmail.com
Abstract: 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.
Keywords: cancer pain, multimodal artificial intelligence, digital twin, pain subtyping, individualized treatment prediction