Emerging technologies in interventional pain management: a scoping review on current innovations and future directions.
Authors
Affiliations (2)
Affiliations (2)
- Department of Neurosurgery (Neuroscience), Shiraz University of Medical Sciences, Shiraz, Iran.
- Educational Supervisor, Motahari Hospital, Shiraz University of Medical Sciences, Marvdasht, Shiraz, Iran. [email protected].
Abstract
Emerging digital and bioelectronic technologies are rapidly transforming interventional pain management, but their clinical roles and evidence base remain unclear. A scoping review of these innovations is needed to map the current landscape and identify gaps for future research. To conduct a scoping review of emerging technologies applied to interventional pain management, and to summarize their clinical applications, outcomes, and methodological limitations. This scoping review followed the PRISMA-ScR guidelines and was structured according to the Population-Concept-Context (PCC) framework. A comprehensive search was performed in PubMed, Scopus, Web of Science, Embase, and CENTRAL from inception to January 1, 2026, using predefined search strategies targeting emerging technologies and interventional pain procedures. Titles, abstracts, and full texts were screened in a three-stage process, supplemented by backward citation searching. Data were charted on study and patient characteristics, technology type, interventional pain application, and reported outcomes. Methodological quality and risk of bias were appraised using Joanna Briggs Institute (JBI) tools appropriate to each study design. Four major themes emerged from the reviewed literature. (1) Precision patient selection and intelligent clinical decision support using multimodal artificial intelligence models, (2) physiologic closed‑loop neuromodulation and neural dosing guided by evoked compound action potentials, (3) AI‑assisted procedural guidance for ultrasound‑guided interventions, and (4) digital therapeutics, wearable technologies, and remote monitoring platforms supporting long‑term management. Across these domains, early evidence suggests that AI‑driven tools and data‑integrated neuromodulation systems may improve patient selection, procedural accuracy, therapy optimization, and longitudinal monitoring in interventional pain care. However, most studies remain limited by small sample sizes, heterogeneous outcome measures, and insufficient external validation. Emerging technologies demonstrate potential for precision, personalization, and effectiveness in interventional pain management. Nevertheless, additional multicenter, longitudinal research with standardized outcomes is essential before extensive clinical implementation.