Aziz Fayha, Bianchini Diletta, Olawade David B, Boussios Stergios
Kent Medway Medical School, University of Kent, Canterbury, U.K.
Kent Oncology Centre, Maidstone General Hospital, Maidstone and Tunbridge Wells NHS Trust, Maidstone, U.K.
Anticancer Res. 2025 Feb;45(2):407-418. doi: 10.21873/anticanres.17430.
The coronavirus disease 2019 (COVID-19) pandemic necessitated a shift in healthcare delivery, emphasizing the need for remote patient monitoring (RPM) to minimize infection risks. This review aimed to evaluate the applications of artificial intelligence (AI) in RPM for cancer patients, exploring its impact on patient outcomes and implications for future healthcare practices. A qualitative systematic review was conducted using keyword searches across four databases: Embase OVID, PubMed, PsychInfo, and Web of Science. After removing duplicates and applying inclusion and exclusion criteria, the selected studies underwent quality assessment using the Critical Appraisal Skills Programme (CASP) tools and a risk of bias assessment. A thematic analysis was then performed using Delve, an application that facilitates deductive coding, to identify and explore themes related to AI in RPM. The search yielded 170 papers, from which 11 quantitative studies were selected for detailed analysis. Deductive coding resulted in the generation of 12 codes, leading to the identification of six subthemes and the construction of two primary themes: Efficacy of the RPM intervention and patient factors. AI systems in RPM show significant potential for enhancing cancer patient care and outcomes. However, this review could not conclusively determine that RPM provides superior outcomes compared to traditional face-to-face care. The findings underscore the preliminary nature of AI in medicine, highlighting the need for larger-scale, long-term studies to fully understand the benefits and limitations of AI in RPM for cancer care.
2019年冠状病毒病（COVID-19）大流行促使医疗服务方式发生转变，强调了远程患者监测（RPM）对于将感染风险降至最低的必要性。本综述旨在评估人工智能（AI）在癌症患者RPM中的应用，探讨其对患者预后的影响以及对未来医疗实践的意义。通过在四个数据库（Embase OVID、PubMed、PsychInfo和Web of Science）中进行关键词搜索，开展了一项定性系统综述。在去除重复项并应用纳入和排除标准后，使用批判性评估技能计划（CASP）工具对所选研究进行质量评估，并进行偏倚风险评估。然后使用Delve（一款有助于演绎编码的应用程序）进行主题分析，以识别和探索与RPM中的AI相关的主题。搜索共得到170篇论文，从中选取了11项定量研究进行详细分析。演绎编码产生了12个代码，从而确定了6个子主题并构建了两个主要主题：RPM干预的效果和患者因素。RPM中的AI系统在改善癌症患者护理和预后方面显示出巨大潜力。然而，本综述无法最终确定RPM与传统面对面护理相比能提供更优的预后。研究结果强调了AI在医学中的初步性质，突出了需要开展更大规模、长期的研究，以全面了解AI在癌症护理RPM中的益处和局限性。
