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

Area of Science:

• Oncology
• Medical Informatics
• Artificial Intelligence

Background:

• Patient-reported outcomes (PROs) are crucial for assessing cancer patient well-being and treatment effects.
• Routine PRO implementation faces challenges like survey fatigue and data interpretation difficulties.
• Existing PRO data, including structured questionnaires and unstructured clinical text, hold untapped potential.

Purpose of the Study:

• To review recent advancements in applying AI to PROs in oncology (2020-2026).
• To explore how AI enhances the analysis and utility of PRO data.
• To discuss the potential of AI-driven insights for proactive patient monitoring and clinical decision-making.

Main Methods:

• Narrative review of literature on AI applications in oncology PROs.
• Analysis of studies using machine learning and natural language processing (NLP) on structured and unstructured PRO data.
• Inclusion of emerging research on non-invasive digital biomarkers (speech, facial expressions).

Main Results:

• AI, including machine learning and NLP, can automate PRO analysis, predict symptom deterioration and treatment toxicity.
• NLP models effectively extract clinically relevant information from free-text patient narratives.
• Non-invasive digital biomarkers show promise in complementing PROs for assessing pain, fatigue, and mood.

Conclusions:

• AI has the potential to transform PROs into dynamic tools for continuous, proactive symptom monitoring.
• AI-driven insights can support oncology decision-making by integrating multimodal patient data.
• Future applications of AI in PROs may lead to more personalized and effective cancer care.