A Review of Voice-Based Pain Detection in Adults Using Artificial Intelligence

Sahar Borna, Clifton R. Haider, Karla C. Maita, Ricardo A. Torres, Francisco R. Avila, John P. Garcia, Gioacchino D. De Sario Velasquez, Christopher J. McLeod, Charles J. Bruce, Rickey E. Carter, Antonio J. Forte

Research output: Contribution to journalReview articlepeer-review


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.

Original languageEnglish (US)
Article number500
Issue number4
StatePublished - Apr 2023


  • machine learning
  • pain assessment
  • vocal biomarkers
  • voice analysis

ASJC Scopus subject areas

  • Bioengineering


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