• Vivian Salama, Brandon Godinich, Yimin Geng, Laia Humbert-Vidan, Laura Maule, Kareem A Wahid, Mohamed A Naser, Renjie He, Abdallah S R Mohamed, Clifton D Fuller, and Amy C Moreno.
• Department of Radiation Oncology (V.S., B.G., L.H.V., L.M., K.A.W., M.A.N., R.H., A.S.R.M., C.D.F., A.C.M), The University of Texas MD Anderson Cancer Center, Houston, TX, USA. Electronic address: dr_vinafawzy@yahoo.com.
• J Pain Symptom Manage. 2024 Dec 1; 68 (6): e462e490e462-e490.

Background/ObjectivesPain is a challenging multifaceted symptom reported by most cancer patients. This systematic review aims to explore applications of artificial intelligence/machine learning (AI/ML) in predicting pain-related outcomes and pain management in cancer.MethodsA comprehensive search of Ovid MEDLINE, EMBASE and Web of Science databases was conducted using terms: "Cancer," "Pain," "Pain Management," "Analgesics," "Artificial Intelligence," "Machine Learning," and "Neural Networks" published up to September 7, 2023. AI/ML models, their validation and performance were summarized. Quality assessment was conducted using PROBAST risk-of-bias andadherence to TRIPOD guidelines.ResultsForty four studies from 2006 to 2023 were included. Nineteen studies used AI/ML for classifying pain after cancer therapy [median AUC 0.80 (range 0.76-0.94)]. Eighteen studies focused on cancer pain research [median AUC 0.86 (range 0.50-0.99)], and 7 focused on applying AI/ML for cancer pain management, [median AUC 0.71 (range 0.47-0.89)]. Median AUC (0.77) of models across all studies. Random forest models demonstrated the highest performance (median AUC 0.81), lasso models had the highest median sensitivity (1), while Support Vector Machine had the highest median specificity (0.74). Overall adherence to TRIPOD guidelines was 70.7%. Overall, high risk-of-bias (77.3%), lack of external validation (14%) and clinical application (23%) was detected. Reporting of model calibration was also missing (5%).ConclusionImplementation of AI/ML tools promises significant advances in the classification, risk stratification, and management decisions for cancer pain. Further research focusing on quality improvement, model calibration, rigorous external clinical validation in real healthcare settings is imperative for ensuring its practical and reliable application in clinical practice.Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.

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