Cracking the Chronic Pain code: A scoping review of Artificial Intelligence in Chronic Pain research

• Published in: Artificial intelligence in medicine Vol. 151; p. 102849
• Main Authors: Khan, Md Asif, Koh, Ryan G.L., Rashidiani, Sajjad, Liu, Theodore, Tucci, Victoria, Kumbhare, Dinesh, Doyle, Thomas E.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.05.2024
• Subjects: AI in chronic pain; Algorithms; Artificial Intelligence; Chronic pain; Chronic Pain - diagnosis; Chronic pain review; Humans; Machine learning; Machine learning in chronic pain; Pain Management - methods; Support Vector Machine; Machine learning in chronic pain; Chronic pain review; AI in chronic pain; Chronic pain; Machine learning
• ISSN: 0933-3657; 1873-2860; 1873-2860
• DOI: 10.1016/j.artmed.2024.102849

The aim of this review is to identify gaps and provide a direction for future research in the utilization of Artificial Intelligence (AI) in chronic pain (CP) management. A comprehensive literature search was conducted using various databases, including Ovid MEDLINE, Web of Science Core Collection, IEEE Xplore, and ACM Digital Library. The search was limited to studies on AI in CP research, focusing on diagnosis, prognosis, clinical decision support, self-management, and rehabilitation. The studies were evaluated based on predefined inclusion criteria, including the reporting quality of AI algorithms used. After the screening process, 60 studies were reviewed, highlighting AI’s effectiveness in diagnosing and classifying CP while revealing gaps in the attention given to treatment and rehabilitation. It was found that the most commonly used algorithms in CP research were support vector machines, logistic regression and random forest classifiers. The review also pointed out that attention to CP mechanisms is negligible despite being the most effective way to treat CP. The review concludes that to achieve more effective outcomes in CP management, future research should prioritize identifying CP mechanisms, CP management, and rehabilitation while leveraging a wider range of algorithms and architectures. This review highlights the potential of AI in improving the management of CP, which is a significant personal and economic burden affecting more than 30% of the world’s population. The identified gaps and future research directions provide valuable insights to researchers and practitioners in the field, with the potential to improve healthcare utilization. •Scoping review of the methodological state-of-the-art of ML in Chronic Pain research.•Identifying Chronic Pain mechanisms are often overlooked despite being the most effective way of treatment.•Current applications of ML in Chronic Pain focus mainly on Diagnosis and Clinical Decision Support.•Supervised ML methods are commonly used and other learning methods are underutilized.•Explainable AI could be leveraged for optimized pain management and informed decision-making.