Application of Artificial Intelligence in Symptom Monitoring in Adult Cancer Survivorship: A Systematic Review.

Tabataba Vakili, S; Haywood, D; Kirk, D; Abdou, AM; Gopalakrishnan, R; Sadeghi, S; Guedes, H; Tan, CJ; Thamm, C; Bernard, R; Wong, HCY; Kuhn, EP; Kwan, JYY; Lee, SF; Hart, NH; Paterson, C; Chopra, DA; Drury, A; Zhang, E; Raeisi Dehkordi, S; Ashbury, FD; Kotronoulas, G; Chow, E; Jefford, M; Chan, RJ; Fazelzad, R; Raman, S; Alkhaifi, M; Multinational Association of Supportive Care in Cancer (MASCC) Survivorship Study Group,

Application of Artificial Intelligence in Symptom Monitoring in Adult Cancer Survivorship: A Systematic Review.

Tabataba Vakili, S Haywood, D

Kirk, D Abdou, AM Gopalakrishnan, R Sadeghi, S Guedes, H Tan, CJ Thamm, C Bernard, R Wong, HCY Kuhn, EP Kwan, JYY Lee, SF Hart, NH Paterson, C Chopra, DA Drury, A Zhang, E Raeisi Dehkordi, S Ashbury, FD Kotronoulas, G Chow, E Jefford, M Chan, RJ Fazelzad, R Raman, S Alkhaifi, M Multinational Association of Supportive Care in Cancer (MASCC) Survivorship Study Group,

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Publisher:: American Society of Clinical Oncology (ASCO)
Publication Type:: Journal Article
Citation:: JCO Clin Cancer Inform, 2024, 8, (8), pp. e2400119
Issue Date:: 2024-12

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Field	Value	Language
dc.contributor.author	Tabataba Vakili, S
dc.contributor.author	Haywood, D https://orcid.org/0000-0002-9317-4135
dc.contributor.author	Kirk, D
dc.contributor.author	Abdou, AM
dc.contributor.author	Gopalakrishnan, R
dc.contributor.author	Sadeghi, S
dc.contributor.author	Guedes, H
dc.contributor.author	Tan, CJ
dc.contributor.author	Thamm, C
dc.contributor.author	Bernard, R
dc.contributor.author	Wong, HCY
dc.contributor.author	Kuhn, EP
dc.contributor.author	Kwan, JYY
dc.contributor.author	Lee, SF
dc.contributor.author	Hart, NH
dc.contributor.author	Paterson, C
dc.contributor.author	Chopra, DA
dc.contributor.author	Drury, A
dc.contributor.author	Zhang, E
dc.contributor.author	Raeisi Dehkordi, S
dc.contributor.author	Ashbury, FD
dc.contributor.author	Kotronoulas, G
dc.contributor.author	Chow, E
dc.contributor.author	Jefford, M
dc.contributor.author	Chan, RJ
dc.contributor.author	Fazelzad, R
dc.contributor.author	Raman, S
dc.contributor.author	Alkhaifi, M
dc.contributor.author	Multinational Association of Supportive Care in Cancer (MASCC) Survivorship Study Group,
dc.date.accessioned	2024-12-12T00:21:28Z
dc.date.available	2024-12-12T00:21:28Z
dc.date.issued	2024-12
dc.identifier.citation	JCO Clin Cancer Inform, 2024, 8, (8), pp. e2400119
dc.identifier.issn	2473-4276
dc.identifier.issn	2473-4276
dc.identifier.uri	http://hdl.handle.net/10453/182482
dc.description.abstract	PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	American Society of Clinical Oncology (ASCO)
dc.relation.ispartof	JCO Clin Cancer Inform
dc.relation.isbasedon	10.1200/CCI.24.00119
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.mesh	Humans
dc.subject.mesh	Artificial Intelligence
dc.subject.mesh	Cancer Survivors
dc.subject.mesh	Neoplasms
dc.subject.mesh	Adult
dc.subject.mesh	Machine Learning
dc.subject.mesh	Survivorship
dc.subject.mesh	Humans
dc.subject.mesh	Neoplasms
dc.subject.mesh	Artificial Intelligence
dc.subject.mesh	Adult
dc.subject.mesh	Machine Learning
dc.subject.mesh	Cancer Survivors
dc.subject.mesh	Survivorship
dc.title	Application of Artificial Intelligence in Symptom Monitoring in Adult Cancer Survivorship: A Systematic Review.
dc.type	Journal Article
utslib.citation.volume	8
utslib.location.activity	United States
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Health
pubs.organisational-group	University of Technology Sydney/Faculty of Health/School of Sport, Exercise and Rehabilitation
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Human Performance Research Centre (HPRC)
utslib.copyright.status	closed_access	*
dc.date.updated	2024-12-12T00:21:26Z
pubs.issue	8
pubs.publication-status	Published
pubs.volume	8
utslib.citation.issue	8

Abstract:

PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/182482