ArticlePain research & management2026
Artificial Intelligence and Connected Technologies in Chronic Pain Management: A Scoping Review.
Article in Pain research & management, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Artificial Intelligence and Connected Technologies in Chronic Pain Management: A Scoping Review.Pain research & management · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundChronic pain affects over 1.5 billion people worldwide, facing persistent challenges in assessment subjectivity, treatment variability, and scalability (i.e., the limited ability to deliver individualized care to large populations within current clinical systems). Artificial intelligence (AI) and connected health technologies have transitioned from experimental tools to clinical reality in pain management.
objectiveThis scoping review maps current AI applications, identifies implementation gaps, and demonstrates the need for systematic technology integration in pain medicine.
methodsWe conducted a targeted literature search across PubMed, Scopus, MEDLINE, Web of Science, and Google Scholar. From 487 identified articles, 23 met inclusion criteria for a detailed analysis. A scoping review approach, following the Arksey and O'Malley framework, provided an integrative perspective on these rapidly evolving applications.
resultsAI implementations span four domains: (1) objective pain assessment via multimodal analysis (facial recognition, neurophysiology, and imaging); (2) personalized treatment strategies using machine learning to optimize therapeutic responses; (3) predictive analytics for risk stratification (postoperative pain and opioid misuse); and (4) real-time monitoring through connected devices and closed-loop neuromodulation. Notable applications include AI-guided spinal cord stimulation and wearable-integrated pain profiling. Despite demonstrable clinical benefits across these domains, integration remains fragmented due to data governance challenges and educational gaps among practitioners.
conclusionsAI is demonstrably improving chronic pain outcomes, yet integration remains fragmented due to data governance issues and educational gaps. Professional societies must establish specialized AI committees, clinical guidelines, and structured training. As this transformation occurs, pain specialists must lead this evolution rather than adapt reactively to external changes.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.