AI-enabled psychometric and psychophysiological assessment in psychiatric comorbidity of oncology patients: A systematic review.

BACKGROUND: The state of psychological distress, including depression and anxiety, is very common among patients with cancer. Common screening tools are usually subjective and limited in terms of their abilities to monitor continuous. AIM: The purpose of the systematic review was to assess the efficiency of artificial intelligence (AI)-based models as detectors of psychological distress among the population of oncology patients. METHOD: A systematic literature search was used in PubMed, Scopus, Web of Science, IEEE Xplore, PsycINFO, and ScienceDirect to find out publications published between 2015 and 2025. There were twelve studies that met the inclusion criteria. RESULTS: A range of machine learning models, such as the Random Forests, Support Vector Machines, and Deep Neural Networks, obtained moderate to high accuracy (AUC = 0.74-0.83) in predicting psychological distress in different cancer types. CONCLUSION: AI-based models have a potential in the early detection of psychological distress among cancer patients. However, the research designs and reporting are heterogeneous, which obstructs the generalizability and clinical application of such results.