Aziz, Fayha, Bianchini, Diletta, Olawade, David B. and Boussios, Stergios (2025) The Impact of AI-driven Remote Patient Monitoring on Cancer Care: A Systematic Review. Anticancer research, 45 (2). pp. 407-418.

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Abstract

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. [Abstract copyright: Copyright © 2025 International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.]

Item Type:	Article
Status:	Published
DOI:	10.21873/anticanres.17430
School/Department:	London Campus
URI:	https://ray.yorksj.ac.uk/id/eprint/11581

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