Using artificial intelligence to improve pain assessment and pain management: a scoping review. (2nd December 2022)

• Record Type: Journal Article
• Authors: Zhang, Meina; Zhu, Linzee; Lin, Shih-Yin; Herr, Keela; Chi, Chih-Lin; Demir, Ibrahim; Dunn Lopez, Karen; Chi, Nai-Ching
• Is Part Of: Journal of the American Medical Informatics Association. Volume 30:Number 3(2023)
• Page Start: 570
• Page End: 587
• Publication Date: 2022-12-02
• Subjects: artificial intelligence -- pain assessment -- pain management -- pain -- pain control; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocac231
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Context: Over 20% of US adults report they experience pain on most days or every day. Uncontrolled pain has led to increased healthcare utilization, hospitalization, emergency visits, and financial burden. Recognizing, assessing, understanding, and treating pain using artificial intelligence (AI) approaches may improve patient outcomes and healthcare resource utilization. A comprehensive synthesis of the current use and outcomes of AI-based interventions focused on pain assessment and management will guide the development of future research. Objectives: This review aims to investigate the state of the research on AI-based interventions designed to improve pain assessment and management for adult patients. We also ascertain the actual outcomes of Al-based interventions for adult patients. Methods: The electronic databases searched include Web of Science, CINAHL, PsycINFO, Cochrane CENTRAL, Scopus, IEEE Xplore, and ACM Digital Library. The search initially identified 6946 studies. After screening, 30 studies met the inclusion criteria. The Critical Appraisals Skills Programme was used to assess study quality. Results: This review provides evidence that machine learning, data mining, and natural language processing were used to improve efficient pain recognition and pain assessment, analyze self-reported pain data, predict pain, and help clinicians and patients to manage chronic pain more effectively. Conclusions: Findings from this review suggest that using AI-based interventions has a positive effect on pain recognition, pain prediction, and pain self-management; however, most reports are only pilot studies. More pilot studies with physiological pain measures are required before these approaches are ready for large clinical trial.