Sanam Tabataba Vakili, Darren Haywood, Deborah Kirk, Aalaa M Abdou, Ragisha Gopalakrishnan, Sarina Sadeghi, Helena Guedes, Chia Jie Tan, Carla Thamm, Rhys Bernard, Henry C Y Wong, Elaine P Kuhn, Jennifer Y Y Kwan, Shing Fung Lee, Nicolas H Hart, Catherine Paterson, Deepti A Chopra, Amanda Drury, Elwyn Zhang, Shayan Raeisi Dehkordi, Fredrick D Ashbury, Grigorios Kotronoulas, Edward Chow, Michael Jefford, Raymond J Chan, Rouhi Fazelzad, Srinivas Raman, Muna Alkhaifi
{"title":"Application of Artificial Intelligence in Symptom Monitoring in Adult Cancer Survivorship: A Systematic Review.","authors":"Sanam Tabataba Vakili, Darren Haywood, Deborah Kirk, Aalaa M Abdou, Ragisha Gopalakrishnan, Sarina Sadeghi, Helena Guedes, Chia Jie Tan, Carla Thamm, Rhys Bernard, Henry C Y Wong, Elaine P Kuhn, Jennifer Y Y Kwan, Shing Fung Lee, Nicolas H Hart, Catherine Paterson, Deepti A Chopra, Amanda Drury, Elwyn Zhang, Shayan Raeisi Dehkordi, Fredrick D Ashbury, Grigorios Kotronoulas, Edward Chow, Michael Jefford, Raymond J Chan, Rouhi Fazelzad, Srinivas Raman, Muna Alkhaifi","doi":"10.1200/CCI.24.00119","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2400119"},"PeriodicalIF":3.3000,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"JCO Clinical Cancer Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1200/CCI.24.00119","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/12/2 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0
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.
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