ReviewCureus2026
Beyond the Algorithm: A Critical and Evidence-Based Review of Artificial Intelligence in Chronic Pain Rehabilitation.
Review in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chronic pain presents a complex, multifactorial challenge in rehabilitation medicine, requiring nuanced, person-centered interventions. Artificial intelligence (AI) has emerged as a potential tool for personalizing diagnosis, predicting outcomes, and optimizing therapy, yet its integration into clinical practice remains fragmented and ethically underdeveloped. This study provides a critical, evidence-based synthesis of AI in chronic pain rehabilitation, drawing on findings from implementation studies, patient-experience research, and equity-by-design initiatives to assess where and how AI can responsibly enhance rehabilitation practices. A narrative review and critical synthesis were conducted using literature from PubMed, Scopus, and Web of Science published between 2015 and 2025. Five thematic domains emerged: spectrum of clinical maturity, patient experience and therapeutic alliance, algorithmic equity and bias, regulatory governance, and economic viability. AI applications ranged from low-touch mobile apps using sensor data to integrated Clinical Decision Support systems. Key barriers included algorithmic opacity, patient emotional burden, such as digital fatigue, biased datasets, and fragmented reimbursement models. Promising pathways included explainable and auditable AI, stakeholder co-creation, and value-based pricing. The findings emphasize that AI adoption success depends less on algorithmic sophistication and more on contextual fit, transparency, and ethical design. AI in pain rehabilitation must evolve from technological novelty to ethical necessity by aligning its development with clinical values and human well-being. Transparent systems, participatory development, dynamic oversight, and equitable access are essential for ensuring that AI enhances rather than amplifies disparities in healthcare.
Indexed as
Identifiers
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.