TY - JOUR
T1 - Application of Artificial Intelligence in Symptom Monitoring in Adult Cancer Survivorship
T2 - A Systematic Review
AU - Tabataba Vakili, Sanam
AU - Haywood, Darren
AU - Kirk, Deborah
AU - Abdou, Aalaa M.
AU - Gopalakrishnan, Ragisha
AU - Sadeghi, Sarina
AU - Guedes, Helena
AU - Tan, Chia Jie
AU - Thamm, Carla
AU - Bernard, Rhys
AU - Wong, Henry C.Y.
AU - Kuhn, Elaine P.
AU - Kwan, Jennifer Y.Y.
AU - Lee, Shing Fung
AU - Hart, Nicolas H.
AU - Paterson, Catherine
AU - Chopra, Deepti A.
AU - Drury, Amanda
AU - Zhang, Elwyn
AU - Raeisi Dehkordi, Shayan
AU - Ashbury, Fredrick D.
AU - Kotronoulas, Grigorios
AU - Chow, Edward
AU - Jefford, Michael
AU - Chan, Raymond J.
AU - Fazelzad, Rouhi
AU - Raman, Srinivas
AU - Alkhaifi, Muna
AU - Multinational Association of Supportive Care in Cancer (MASCC) Survivorship Study Group
PY - 2024/12
Y1 - 2024/12
N2 - 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.
AB - 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.
KW - artificial intelligence
KW - cancer
KW - symptom monitoring
KW - patient-centered care
UR - http://www.scopus.com/inward/record.url?scp=85211409108&partnerID=8YFLogxK
U2 - 10.1200/CCI.24.00119
DO - 10.1200/CCI.24.00119
M3 - Review article
C2 - 39621952
AN - SCOPUS:85211409108
SN - 2473-4276
VL - 8
JO - JCO clinical cancer informatics
JF - JCO clinical cancer informatics
M1 - e2400119
ER -
