Tabataba Vakili Sanam, Haywood Darren, Kirk Deborah, Abdou Aalaa M, Gopalakrishnan Ragisha, Sadeghi Sarina, Guedes Helena, Tan Chia Jie, Thamm Carla, Bernard Rhys, Wong Henry C Y, Kuhn Elaine P, Kwan Jennifer Y Y, Lee Shing Fung, Hart Nicolas H, Paterson Catherine, Chopra Deepti A, Drury Amanda, Zhang Elwyn, Raeisi Dehkordi Shayan, Ashbury Fredrick D, Kotronoulas Grigorios, Chow Edward, Jefford Michael, Chan Raymond J, Fazelzad Rouhi, Raman Srinivas, Alkhaifi Muna
Department of Medical Oncology & Hematology, Odette Cancer Centre, Sunnybrook Health Science Centre, Toronto, ON, Canada.
Human Performance Research Centre, INSIGHT Research Institute, University of Technology Sydney (UTS), Sydney, NSW, Australia.
JCO Clin Cancer Inform. 2024 Dec;8:e2400119. doi: 10.1200/CCI.24.00119. Epub 2024 Dec 2.
The adoption of artificial intelligence (AI) in health care may afford new avenues for personalized and patient-centered care. This systematic review explored the role of AI in symptom monitoring for adult cancer survivors.
A comprehensive search was performed from inception to November 2023 in seven bibliographic databases and three clinical trial registries. This PROSPERO registered review (ID: CRD42023476027) assessed reports of empirical research studies of AI use in symptom monitoring (physical and psychological symptoms) across all cancer types in adults.
A total of 18,530 reports were identified, of which 41 met review criteria and were analyzed. Included studies were predominantly published between 2021 and 2023, originated in the United States (39.0%) and Japan (14.6%), and primarily used cohort designs (80.5%), followed by cross-sectional designs (12.2%). The mean sample size was 617.14 (standard deviation = 1,401.37), with most studies primarily including multiple tumor types (31.7%) or breast cancer survivors (26.8%). Machine learning algorithms (43.9%) was the most used AI method, followed by natural language processing (29.3%), AI-driven chatbots (17.1%), and decision support tools (9.8%). The most common inputs to the AI algorithms were textual data, patient-reported symptoms, and physiologic measurements. The most examined symptom was pain (34.2% of studies), followed by fatigue and nausea (17.1% of studies each). Overall, the review showed increasing AI technology use in the prediction and monitoring of cancer symptoms.
AI is being used to enhance symptom monitoring in various cancer settings. When considering integration into clinical practice, standardization of data capture, the use of analytics, investing in infrastructure, and the end-user experience should be considered for successful implementation and monitoring the improvement of patient outcomes.
在医疗保健中采用人工智能（AI）可能为个性化和以患者为中心的护理提供新途径。本系统评价探讨了AI在成年癌症幸存者症状监测中的作用。
从数据库建库至2023年11月，在7个文献数据库和3个临床试验注册库中进行了全面检索。这项在PROSPERO注册的评价（注册号：CRD42023476027）评估了关于AI用于成年患者所有癌症类型症状监测（身体和心理症状）的实证研究报告。
共识别出18530篇报告，其中41篇符合评价标准并进行了分析。纳入的研究主要发表于2021年至2023年，起源于美国（39.0%）和日本（14.6%），主要采用队列设计（80.5%），其次是横断面设计（12.2%）。平均样本量为617.14（标准差=1401.37），大多数研究主要纳入多种肿瘤类型（31.7%）或乳腺癌幸存者（26.8%）。机器学习算法（43.9%）是最常用的AI方法，其次是自然语言处理（29.3%）、AI驱动的聊天机器人（17.1%）和决策支持工具（9.8%）。AI算法最常见的输入是文本数据、患者报告的症状和生理测量值。研究最多的症状是疼痛（占研究的34.2%），其次是疲劳和恶心（各占研究的17.1%）。总体而言，该评价表明AI技术在癌症症状预测和监测中的应用不断增加。
AI正被用于加强各种癌症环境下的症状监测。在考虑将其整合到临床实践中时，为了成功实施并监测患者预后的改善，应考虑数据采集的标准化、分析方法的使用、基础设施投资以及最终用户体验。
