Introduction and background

Chronic pain affects over 20% of the adult population in the United States alone and is a leading contributor to disability worldwide [1]. Defined by its persistence beyond typical healing times and the significant emotional distress or functional disability it causes, chronic pain requires comprehensive management [2]. Because it is often resistant to standard pharmacological therapies, chronic pain necessitates multimodal and personalized rehabilitation strategies that span the biopsychosocial spectrum [3]. The integration of artificial Intelligence (AI) and machine learning (ML) offers the potential for personalized diagnosis, predictive outcome modeling, and optimized therapeutic delivery [4]. The convergence of human clinical expertise with computational precision - often termed "high-performance medicine" - has already shown vast potential across medical disciplines [5]. AI-driven predictive modeling is increasingly being utilized to forecast post-surgical pain and opioid utilization based on complex, high-dimensional clinical data [6]. Despite this promise, widespread clinical adoption in rehabilitation is lagging, constrained by ethical design limitations, fragmented implementation, and the need for robust evidence of real-world performance. This paper conducts a critical and evidence-based narrative review of AI in pain management. By synthesizing contemporary literature focusing on implementation science, patient-centered outcomes, and algorithmic equity, we evaluate AI through the lenses of clinical maturity, patient experience, algorithmic justice, regulatory preparedness, and economic scalability.