Artificial Intelligence for Clinical Decision Support in Rural Spine Care: A Narrative Review

Abstract

Background/Objectives: Artificial intelligence (AI) is increasingly applied across spinal pain care, by improving diagnostic accuracy, optimizing clinical workflow, and advancing translational research. However, AI’s role and its integration into rural spine care face challenges. Therefore, the current narrative review synthesizes the role of AI in rural spine care, spanning diagnosis, clinical decision support, imaging, natural-language processing, and remote monitoring. Methods: We searched the peer-reviewed literature on AI in spinal pain care across six databases from inception to June 2026. Results: In the United States, AI models have shown promise in strengthening clinical decision support that assists clinicians in identifying spinal pain disorders and providing evidence-based treatment recommendations; however, most were developed and validated in urban healthcare settings. Additionally, evidence on external validation, dataset representativeness, robustness to incomplete or low-quality data, interoperability, prospective clinical utility, and implementation feasibility in rural spine care remains limited. Therefore, we propose a translational framework in which de-identified data from rural and urban healthcare settings are aggregated, harmonized, and used to develop a multimodal AI model. AI models further require rigorous technical and external validation, prospective validation in rural settings, explainability, and continuous post-implementation monitoring. Despite its benefits, key limitations, including data scarcity and algorithmic bias, are also highlighted. Conclusions: AI has emerged as a promising tool for strengthening clinical decision support in spinal pain disorders in rural settings. The most realistic role of AI in rural settings is not to replace specialist expertise, but to act as one component of a multidisciplinary care team.