Artificial Intelligence for Patient-Reported Outcomes in Oncology: Current Applications and Future Directions Toward Multimodal Monitoring

Patient-reported outcomes (PROs) are an integral component of contemporary oncology. They provide direct insight into symptom severity, treatment tolerability, and health-related quality of life. Despite their clinical relevance, routine implementation faces several hurdles. Key limitations include patient survey fatigue, challenges in real-time interpretation of complex symptom trajectories, and incomplete longitudinal data that limit reliable analysis. This narrative review summarizes recent advances (2020-2026) in applying artificial intelligence (AI) to structured questionnaires, including EORTC QLQ-C30, PROMIS, and PRO-CTCAE, as well as to unstructured clinical text. Machine learning and natural language processing may enhance the clinical utility of PROs through automated analysis, symptom extraction, and predictive modeling. Current studies suggest that AI-based approaches can support the prediction of symptom deterioration, treatment-related toxicity, and healthcare utilization, including unplanned hospitalizations and emergency department visits. Furthermore, NLP models can extract clinically meaningful information from free-text narratives. We also discuss emerging non-invasive digital biomarkers derived from speech and facial expressions. Multimodal approaches suggest that these features may provide complementary indicators of pain, fatigue, and affective state. Overall, AI has the potential to transform PROs from static assessment tools into dynamic clinical instruments. This shift may enable more continuous and proactive symptom monitoring and support the integration of multimodal patient data into oncology decision-making workflows.