大多数癌症患者报告疼痛是一种具有挑战性的多方面症状。本系统综述旨在探索人工智能/机器学习 (AI/ML) 在预测癌症疼痛相关结果和疼痛管理方面的应用。使用以下术语对 Ovid MEDLINE、EMBASE 和 Web of Science 数据库进行了全面搜索：“癌症” ”、“疼痛”、“疼痛管理”、“镇痛药”、“人工智能”、“机器学习”和“神经网络”，截至 2023 年 9 月 7 日出版。对 AI/ML 模型及其验证和性能进行了总结。使用 PROBAST 偏倚风险并遵守 TRIPOD 指南进行质量评估。纳入了 2006 年至 2023 年的 44 项研究。十九项研究使用 AI/ML 对癌症治疗后的疼痛进行分类 [中位 AUC 0.80（范围 0.76-0.94）]。 18 项研究重点关注癌症疼痛研究 [中位 AUC 0.86（范围 0.50-0.99）]，7 项研究重点关注应用 AI/ML 进行癌症疼痛管理，[中位 AUC 0.71（范围 0.47-0.89）]。所有研究模型的中位 AUC (0.77)。随机森林模型表现出最高的性能（中值 AUC 0.81），套索模型具有最高的中值敏感性（1），而支持向量机具有最高的中值特异性（0.74）。 TRIPOD 指南的总体遵守率为 70.7%。总体而言，检测到高偏倚风险（77.3%）、缺乏外部验证（14%）和临床应用（23%）。模型校准报告也缺失（5%）。人工智能/机器学习工具的实施有望在癌痛的分类、风险分层和管理决策方面取得重大进展。为了确保其在临床实践中的实际和可靠应用，必须进一步关注实际医疗保健环境中的质量改进、模型校准和严格的外部临床验证。版权所有 © 2024。由 Elsevier Inc. 出版。

Pain is a challenging multifaceted symptom reported by most cancer patients. This systematic review aims to explore applications of artificial intelligence/machine learning (AI/ML) in predicting pain-related outcomes and pain management in cancer.A comprehensive search of Ovid MEDLINE, EMBASE and Web of Science databases was conducted using terms: "Cancer", "Pain", "Pain Management", "Analgesics", "Artificial Intelligence", "Machine Learning", and "Neural Networks" published up to September 7, 2023. AI/ML models, their validation and performance were summarized. Quality assessment was conducted using PROBAST risk-of-bias andadherence to TRIPOD guidelines.Forty four studies from 2006-2023 were included. Nineteen studies used AI/ML for classifying pain after cancer therapy [median AUC 0.80 (range 0.76-0.94)]. Eighteen studies focused on cancer pain research [median AUC 0.86 (range 0.50-0.99)], and 7 focused on applying AI/ML for cancer pain management, [median AUC 0.71 (range 0.47-0.89)]. Median AUC (0.77) of models across all studies. Random forest models demonstrated the highest performance (median AUC 0.81), lasso models had the highest median sensitivity (1), while Support Vector Machine had the highest median specificity (0.74). Overall adherence to TRIPOD guidelines was 70.7%. Overall, high risk-of-bias (77.3%), lack of external validation (14%) and clinical application (23%) was detected. Reporting of model calibration was also missing (5%).Implementation of AI/ML tools promises significant advances in the classification, risk stratification, and management decisions for cancer pain. Further research focusing on quality improvement, model calibration, rigorous external clinical validation in real healthcare settings is imperative for ensuring its practical and reliable application in clinical practice.Copyright © 2024. Published by Elsevier Inc.