Use of Artificial Intelligence as a Clinical Decision Support System in the Management of Chronic Low Back Pain. A Scoping Review with Applied Conceptual Component
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Abstract
Background and Objectives: The high prevalence of chronic low back pain and the growing demand for care require more efficient physiotherapy management models. Artificial Intelligence (AI) is presented as a support tool, although its clinical integration raises ethical and technical questions. The main objective was to review the recent literature on clinical decision support systems (CDSS) in lumbar physiotherapy and to explain their implications for the design of supervised clinical assistants.
Methods: A scoping review with a conceptual component was conducted using structured searches in PubMed, Web of Science, Scopus, Cochrane, and PEDro. Original studies published in the last 5 years that applied machine learning algorithms or digital tools in the management of low back pain were selected.
Results: Evidence indicates that machine learning algorithms have a high predictive capacity for triage and functional prognosis, showing equivalent or sometimes superior performance to conventional tools. In the therapeutic field, digital interventions show positive results for adherence and remote monitoring, although their direct efficacy on pain varies. The lack of transparency of algorithms was identified as the main barrier to trust.
Discussion: The results suggest that the current usefulness of AI lies in safety filtering and patient stratification, rather than in autonomous prescribing. The need to implement models with mandatory human supervision to mitigate risks is discussed. As a practical implication of the review, the theoretical feasibility of assistants based on strict clinical rules for detecting red flags and on RAG (Retrieval-Augmented Generation) architectures to personalize education without hallucinations is proposed.
Conclusions: AI is a valid technology for supporting triage and optimizing resources, but it requires external validation and transparency. The profession must lead the integration of these digital "co-pilots" to offload bureaucratic tasks and strengthen the therapeutic alliance, avoiding unsupervised automation.
Keywords
- Artificial Intelligence
- Clinical Decision Support Systems
- Physiotherapy
- chronic low back pain
- Machine Learning
- Telerehabilitation
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