Leveraging voice biomarkers to quantify chronic pain: a rapid review.

This rapid review examines the emerging role of voice-based artificial intelligence (AI) technologies in the objective assessment of chronic pain. It highlights promising applications of vocal biomarkers in pain quantification, particularly for populations with communication challenges or poorly understood conditions. AI is transforming healthcare, particularly for early detection of cardiovascular diseases and some neurological disorders, and it holds promise for managing chronic pain. This rapid literature review explores the potential of voice-based AI technologies to identify and analyze biomarkers that can objectively assess pain for populations with chronic pain conditions. These conditions are often complex and would benefit from more precise, reproducible measures of pain. While traditional pain scales heavily rely on self-reports, voice biomarkers are a non-invasive, scalable alternative. Studies show that changes in vocal characteristics-such as pitch, loudness, and jitter-correlate with pain intensity and quality; therefore, they offer insights that traditional, subjective measures may overlook. Machine learning models applied to voice data have demonstrated promise in detecting pain, particularly in vulnerable populations, such as those with intellectual and developmental disabilities. The review highlights how AI-driven voice analysis can complement cognitive behavioral therapy in pain management, enhancing accessibility and clinical outcomes. Despite the promise of AI-based approaches, challenges remain in standardizing these technologies for routine clinical use. Future research is needed to validate voice biomarkers across diverse pain conditions and to integrate them into clinical workflows to improve early diagnosis and personalized care, thus offering an innovative approach to chronic pain management. Key words: Voice biomarkers, Artificial intelligence, Pain Management, Machine Learning, Chronic Pain, Digital Health, Objective Pain Assessment.