ReviewBioengineering (Basel, Switzerland)2023
A Review of Voice-Based Pain Detection in Adults Using Artificial Intelligence.
Review in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it, 27 citations in OpenAlex.
- AI in Medical Questionnaires: Scoping ReviewJournal of medical Internet research · 2025Pooled it
- Application of AI in Multilevel Pain Assessment Using Facial Images: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2024Pooled it
- Current state of research and future developments of artificial intelligence in pain diagnosis and treatment.Journal of translational medicine · 2026Review
- Voice-Based Pain Level Classification for Sensor-Assisted Intelligent Care.Sensors (Basel, Switzerland) · 2026Article
- The Fifth Edition of the Automated Assessment of Pain (AAP 2025).Proceedings of the ... ACM International Conference on Multimodal Interaction. ICMI (Conference) · 2025Article
- What can speech tell us about pain?Pain reports · 2025Article
- Method Matters: Enhancing Voice-Based Depression Detection With a New Data Collection Framework.Depression and anxiety · 2025Article
- The Bridge2AI-voice application: initial feasibility study of voice data acquisition through mobile health.Frontiers in digital health · 2025Article
- Pain Management in Cancer Patients With Artificial Intelligence: Narrative Review.Scientifica · 2025Review
- Leveraging voice biomarkers to quantify chronic pain: a rapid review.Frontiers in pain research (Lausanne, Switzerland) · 2025Review
- Pain Research and Treatment: Policy Implications.Policy insights from the behavioral and brain sciences · 2024Article
- Voice EHR: introducing multimodal audio data for health.Frontiers in digital health · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pain is a complex and subjective experience, and traditional methods of pain assessment can be limited by factors such as self-report bias and observer variability. Voice is frequently used to evaluate pain, occasionally in conjunction with other behaviors such as facial gestures. Compared to facial emotions, there is less available evidence linking pain with voice. This literature review synthesizes the current state of research on the use of voice recognition and voice analysis for pain detection in adults, with a specific focus on the role of artificial intelligence (AI) and machine learning (ML) techniques. We describe the previous works on pain recognition using voice and highlight the different approaches to voice as a tool for pain detection, such as a human effect or biosignal. Overall, studies have shown that AI-based voice analysis can be an effective tool for pain detection in adult patients with various types of pain, including chronic and acute pain. We highlight the high accuracy of the ML-based approaches used in studies and their limitations in terms of generalizability due to factors such as the nature of the pain and patient population characteristics. However, there are still potential challenges, such as the need for large datasets and the risk of bias in training models, which warrant further research.
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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.