Potential applications of artificial intelligence in pain management: a scoping review
Abstract
Introduction: Pain management remains a critical challenge in clinical settings. This scoping review studied the applications of artificial intelligence (AI) in pain medicine, particularly pain detection, prediction, and classification.
Methods: A comprehensive search was conducted in PubMed, Scopus, and Cochrane Library databases up to July 2025.
Results: Forty-seven relevant studies were included. The findings demonstrated that AI may have the potential for objective pain assessment using facial expressions, electroencephalography, neuroimaging, and physiological signals. Detection models achieved high accuracy, frequently exceeding 85%-90%. Predictive models showed efficient forecasting in pain sensitivity, pain evaluation after operation, and pain treatment response. Classification approaches also reported high performance. The results are hindered by several limitations: risk of overfitting, shortage of real-world experiments, homogeneous or small data samples, and methodological heterogeneity. Critical barriers for clinical implementation are ethical concerns and external validity.
Conclusion: AI showed significant potential as a pain assessment and decision-support tool in pain medicine and research, especially for populations unable to self-report. It is recommended to use large, diverse datasets and real-world clinical validation to ensure the effective transitioning of artificial intelligence into healthcare systems.
How strong is the evidence?
Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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