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Artificial intelligence empowered digital health technologies in cancer survivorship care: A scoping review

OBJECTIVE: The objectives of this systematic review are to describe features and specific application scenarios for current cancer survivorship care services of Artificial intelligence (AI)-driven digital health technologies (DHTs) and to explore the acceptance and briefly evaluate its feasibility i...

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Autores principales: Pan, Lu-Chen, Wu, Xiao-Ru, Lu, Ying, Zhang, Han-Qing, Zhou, Yao-Ling, Liu, Xue, Liu, Sheng-Lin, Yan, Qiao-Yuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9513729/
https://www.ncbi.nlm.nih.gov/pubmed/36176267
http://dx.doi.org/10.1016/j.apjon.2022.100127
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author Pan, Lu-Chen
Wu, Xiao-Ru
Lu, Ying
Zhang, Han-Qing
Zhou, Yao-Ling
Liu, Xue
Liu, Sheng-Lin
Yan, Qiao-Yuan
author_facet Pan, Lu-Chen
Wu, Xiao-Ru
Lu, Ying
Zhang, Han-Qing
Zhou, Yao-Ling
Liu, Xue
Liu, Sheng-Lin
Yan, Qiao-Yuan
author_sort Pan, Lu-Chen
collection PubMed
description OBJECTIVE: The objectives of this systematic review are to describe features and specific application scenarios for current cancer survivorship care services of Artificial intelligence (AI)-driven digital health technologies (DHTs) and to explore the acceptance and briefly evaluate its feasibility in the application process. METHODS: Search for literatures published from 2010 to 2022 on sites MEDLINE, IEEE-Xplor, PubMed, Embase, Cochrane Central Register of Controlled Trials and Scopus systematically. The types of literatures include original research, descriptive study, randomized controlled trial, pilot study, and feasible or acceptable study. The literatures above described current status and effectiveness of digital medical technologies based on AI and used in cancer survivorship care services. Additionally, we use QuADS quality assessment tool to evaluate the quality of literatures included in this review. RESULTS: 43 studies that met the inclusion criteria were analyzed and qualitatively synthesized. The current status and results related to the application of AI-driven DHTs in cancer survivorship care were reviewed. Most of these studies were designed specifically for breast cancer survivors’ care and focused on the areas of recurrence or secondary cancer prediction, clinical decision support, cancer survivability prediction, population or treatment stratified, anti-cancer treatment-induced adverse reaction prediction, and so on. Applying AI-based DHTs to cancer survivors actually has shown some positive outcomes, including increased motivation of patient-reported outcomes (PROs), reduce fatigue and pain levels, improved quality of life, and physical function. However, current research mostly explored the technology development and formation (testing) phases, with limited-scale population, and single-center trial. Therefore, it is not suitable to draw conclusions that the effectiveness of AI-based DHTs in supportive cancer care, as most of applications are still in the early stage of development and feasibility testing. CONCLUSIONS: While digital therapies are promising in the care of cancer patients, more high-quality studies are still needed in the future to demonstrate the effectiveness of digital therapies in cancer care. Studies should explore how to develop uniform standards for measuring patient-related outcomes, ensure the scientific validity of research methods, and emphasize patient and health practitioner involvement in the development and use of technology.
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spelling pubmed-95137292022-09-28 Artificial intelligence empowered digital health technologies in cancer survivorship care: A scoping review Pan, Lu-Chen Wu, Xiao-Ru Lu, Ying Zhang, Han-Qing Zhou, Yao-Ling Liu, Xue Liu, Sheng-Lin Yan, Qiao-Yuan Asia Pac J Oncol Nurs Review OBJECTIVE: The objectives of this systematic review are to describe features and specific application scenarios for current cancer survivorship care services of Artificial intelligence (AI)-driven digital health technologies (DHTs) and to explore the acceptance and briefly evaluate its feasibility in the application process. METHODS: Search for literatures published from 2010 to 2022 on sites MEDLINE, IEEE-Xplor, PubMed, Embase, Cochrane Central Register of Controlled Trials and Scopus systematically. The types of literatures include original research, descriptive study, randomized controlled trial, pilot study, and feasible or acceptable study. The literatures above described current status and effectiveness of digital medical technologies based on AI and used in cancer survivorship care services. Additionally, we use QuADS quality assessment tool to evaluate the quality of literatures included in this review. RESULTS: 43 studies that met the inclusion criteria were analyzed and qualitatively synthesized. The current status and results related to the application of AI-driven DHTs in cancer survivorship care were reviewed. Most of these studies were designed specifically for breast cancer survivors’ care and focused on the areas of recurrence or secondary cancer prediction, clinical decision support, cancer survivability prediction, population or treatment stratified, anti-cancer treatment-induced adverse reaction prediction, and so on. Applying AI-based DHTs to cancer survivors actually has shown some positive outcomes, including increased motivation of patient-reported outcomes (PROs), reduce fatigue and pain levels, improved quality of life, and physical function. However, current research mostly explored the technology development and formation (testing) phases, with limited-scale population, and single-center trial. Therefore, it is not suitable to draw conclusions that the effectiveness of AI-based DHTs in supportive cancer care, as most of applications are still in the early stage of development and feasibility testing. CONCLUSIONS: While digital therapies are promising in the care of cancer patients, more high-quality studies are still needed in the future to demonstrate the effectiveness of digital therapies in cancer care. Studies should explore how to develop uniform standards for measuring patient-related outcomes, ensure the scientific validity of research methods, and emphasize patient and health practitioner involvement in the development and use of technology. Elsevier 2022-08-23 /pmc/articles/PMC9513729/ /pubmed/36176267 http://dx.doi.org/10.1016/j.apjon.2022.100127 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Review
Pan, Lu-Chen
Wu, Xiao-Ru
Lu, Ying
Zhang, Han-Qing
Zhou, Yao-Ling
Liu, Xue
Liu, Sheng-Lin
Yan, Qiao-Yuan
Artificial intelligence empowered digital health technologies in cancer survivorship care: A scoping review
title Artificial intelligence empowered digital health technologies in cancer survivorship care: A scoping review
title_full Artificial intelligence empowered digital health technologies in cancer survivorship care: A scoping review
title_fullStr Artificial intelligence empowered digital health technologies in cancer survivorship care: A scoping review
title_full_unstemmed Artificial intelligence empowered digital health technologies in cancer survivorship care: A scoping review
title_short Artificial intelligence empowered digital health technologies in cancer survivorship care: A scoping review
title_sort artificial intelligence empowered digital health technologies in cancer survivorship care: a scoping review
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9513729/
https://www.ncbi.nlm.nih.gov/pubmed/36176267
http://dx.doi.org/10.1016/j.apjon.2022.100127
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