Clinical Associate Professor, Program Director, Family Medicine Chronic Pain Enhanced Skills R3, Department of Family Medicine, University of Calgary, Calgary, AB.
Abstract:Background: Artificial intelligence (AI) is increasingly being incorporated into the evaluation and management of chronic low back pain (cLBP), with applications ranging from diagnostic imaging to clinical decision support and digital therapeutics. Objective: To review current evidence regarding the role of AI in the assessment and management of cLBP and discuss its potential benefits, limitations, and future clinical applications. Methods: A narrative review of recent peer-reviewed literature examining AI-assisted diagnostic tools, machine learning prediction models, clinical decision support systems, and AI-enabled therapeutic interventions. Results: AI demonstrates promising performance in lumbar imaging interpretation, prognostic modelling, and individualized digital self-management. However, most applications remain in early clinical development, with limited external validation, concerns regarding bias, interpretability, privacy, and regulatory oversight. Conclusions: AI should currently be viewed as an adjunct to—not a replacement for—clinical judgement. Broader adoption will require rigorous validation, transparent governance, and continued emphasis on patient-centred care.
Key Words: artificial intelligence; chronic low back pain; digital therapeutics; clinical decision support; musculoskeletal medicine; primary care.
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• AI applications in cLBP have advanced rapidly, particularly in imaging analysis, prognostic modelling, and digital therapeutics.
• Most published algorithms require further external validation before routine clinical implementation.
• AI should augment—not replace—clinical judgement.
• The successful integration of AI into musculoskeletal care will depend on rigorous validation, transparent governance, equitable implementation, and continued emphasis on patient-centred care.
1. AI currently performs best as a clinical decision-support tool, not an autonomous clinician.
2. Automated lumbar MRI interpretation has achieved diagnostic accuracy approaching expert radiologists but must always be interpreted in clinical context.
3. AI-enabled digital therapeutics can modestly improve pain-related disability and patient self-management when used alongside usual care.
4. Clinicians should remain vigilant regarding algorithmic bias, limited external validation, privacy concerns, and generative AI hallucinations.
5. Human clinical judgement, empathy, and shared decision-making remain indispensable in the management of chronic low back pain.
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