ReviewFrontiers in pain research (Lausanne, Switzerland)2025
Leveraging voice biomarkers to quantify chronic pain: a rapid review.
Review in Frontiers in pain research (Lausanne, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07743905 (Virtual Pain Neuroscience Education for Patients Undergoing Spinal Cord Stimulation), which is not on this map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Virtual Pain Neuroscience Education for Patients Undergoing Spinal Cord Stimulation: A Hybrid Effectiveness-Implementation Pilot Trial
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.