Background/objective: Pain is a challenging multifaceted symptom reported by most cancer patients, resulting in a substantial burden on both patients and healthcare systems. This systematic review aims to explore applications of artificial intelligence/machine learning (AI/ML) in predicting pain-related outcomes and supporting decision-making processes in pain management in cancer. Methods: A comprehensive search of Ovid MEDLINE, EMBASE and Web of Science databases was conducted using terms including Cancer, Pain, Pain Management, Analgesics, Opioids, Artificial Intelligence, Machine Learning, Deep Learning, and Neural Networks, published up to September 7, 2023. The screening process was performed using the Covidence screening tool. Only original studies conducted in human cohorts were included. AI/ML models, their validation and performance and adherence to TRIPOD guidelines were summarized from the final included studies. Results: This systematic review included 44 studies from 2006-2023. Most studies were prospective and uni-institutional. There was an increase in the trend of AI/ML studies in cancer pain in the last 4 years. Nineteen studies used AI/ML for classifying cancer patients pain development after cancer therapy, with median AUC 0.80 (range 0.76-0.94). Eighteen studies focused on cancer pain research with median AUC 0.86 (range 0.50-0.99), and 7 focused on applying AI/ML for cancer pain management decisions with median AUC 0.71 (range 0.47-0.89). Multiple ML models were investigated with. median AUC across all models in all studies (0.77). 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 of included studies to TRIPOD guidelines was 70.7%. Lack of external validation (14%) and clinical application (23%) of most included studies was detected. Reporting of model calibration was also missing in the majority of studies (5%). Conclusion: Implementation of various novel AI/ML tools promises significant advances in the classification, risk stratification, and management decisions for cancer pain. These advanced tools will integrate big health-related data for personalized pain management in cancer patients. Further research focusing on model calibration and rigorous external clinical validation in real healthcare settings is imperative for ensuring its practical and reliable application in clinical practice.

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癌症相关疼痛中的人工智能和机器学习：系统评价

背景/目的：疼痛是大多数癌症患者报告的一种具有挑战性的多方面症状，给患者和医疗保健系统带来沉重负担。本系统综述旨在探索人工智能/机器学习 (AI/ML) 在预测疼痛相关结果和支持癌症疼痛管理决策过程中的应用。方法：使用截至 9 月 7 日发布的癌症、疼痛、疼痛管理、镇痛药、阿片类药物、人工智能、机器学习、深度学习和神经网络等术语对 Ovid MEDLINE、EMBASE 和 Web of Science 数据库进行了全面检索。 2023 年。筛查过程是使用 Covidence 筛查工具进行的。仅包括在人类队列中进行的原始研究。最终纳入的研究总结了 AI/ML 模型、其验证和性能以及对 TRIPOD 指南的遵守情况。结果：本系统评价包括 2006 年至 2023 年的 44 项研究。大多数研究都是前瞻性的、单一机构的。过去 4 年，癌症疼痛方面的 AI/ML 研究呈增长趋势。19 项研究使用 AI/ML 对癌症治疗后癌症患者的疼痛发展进行分类，中位 AUC 为 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 进行癌症疼痛管理决策，中位 AUC 0.71（范围 0.47-0.89）。研究了多个 ML 模型。所有研究中所有模型的中位 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）。纳入的研究对 TRIPOD 指南的总体遵守率为 70.7%。大多数纳入的研究都缺乏外部验证（14%）和临床应用（23%）。大多数研究中也缺少模型校准报告（5%）。结论：各种新型 AI/ML 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。这些先进的工具将整合与健康相关的大数据，以对癌症患者进行个性化疼痛管理。为了确保其在临床实践中的实际和可靠应用，必须进一步关注模型校准和在真实医疗保健环境中严格的外部临床验证。