Application of Artificial Intelligence in Symptom Monitoring in Adult Cancer Survivorship: A Systematic Review
Sanam Tabataba Vakili, Darren Haywood, Deborah Walker, Aalaa Abdou, Ragisha Gopalakrishnan, Sarina Sadeghi, Helena Karolyne Arruda Guedes, Chia Jie Tan, Carla Thamm, Rhys Bernard, Henry C. Y. Wong, Elaine P. Kuhn, Jennifer Kwan, Shing Fung Lee, Nicolas H. Hart, Catherine F Paterson, Deepti 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
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.