Application of Artificial Intelligence in Symptom Monitoring in Adult Cancer Survivorship: A Systematic Review
- Author(s)
- 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;
- Journal Title
- JCO Clinical Cancer Informatics
- Publication Type
- Review
- 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.
- Publisher
- American Society of Clinical Oncology
- Keywords
- Humans; *Artificial Intelligence; *Cancer Survivors/psychology; *Neoplasms/psychology/therapy; Adult; Machine Learning; Survivorship
- Department(s)
- Health Services Research
- Publisher's Version
- https://doi.org/10.1200/cci.24.00119
- Terms of Use/Rights Notice
- Refer to copyright notice on published article.
Creation Date: 2024-12-19 05:48:18
Last Modified: 2024-12-19 05:48:41