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

Area of Science:

• Biomedical Engineering
• Artificial Intelligence in Healthcare
• Pain Management Research

Background:

• Traditional pain assessment methods face limitations like self-report bias and observer variability.
• Voice analysis, though less studied than facial expressions, shows potential for pain evaluation.
• Existing research on voice-based pain detection requires synthesis, especially concerning advanced computational techniques.

Purpose of the Study:

• To review and synthesize current research on voice recognition and analysis for adult pain detection.
• To specifically examine the role of artificial intelligence (AI) and machine learning (ML) in this domain.
• To highlight different approaches and identify challenges and future research directions.

Main Methods:

• Literature review synthesizing studies on voice analysis for pain detection in adults.
• Focus on research employing artificial intelligence (AI) and machine learning (ML) techniques.
• Analysis of voice as a biosignal for pain recognition, considering human effects.

Main Results:

• AI-based voice analysis demonstrates effectiveness in detecting various types of adult pain (chronic and acute).
• Machine learning (ML) approaches show high accuracy in pain detection from voice data.
• Limitations include generalizability issues due to pain characteristics and patient population variability.

Conclusions:

• AI and ML offer a promising, objective tool for pain detection, complementing traditional methods.
• Further research is needed to address challenges such as the requirement for large datasets and potential model bias.
• Improving generalizability of AI/ML voice-based pain detection models is crucial for clinical application.