ReviewNPJ digital medicine2020
Artificial intelligence to improve back pain outcomes and lessons learnt from clinical classification approaches: three systematic reviews.
Review in NPJ digital medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 3 of them syntheses that pooled it.
What it found
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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.
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Who cites it
41 citing papers in PubMed, 3 syntheses or guidelines pooled it, 103 citations in OpenAlex.
- Prevention and management of degenerative lumbar spine disorders through artificial intelligence-based decision support systems: a systematic review.BMC musculoskeletal disorders · 2025Pooled it
- The Use of Sensors to Prevent, Predict Transition to Chronic and Personalize Treatment of Low Back Pain: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Artificial Intelligence and Computer Vision in Low Back Pain: A Systematic Review.International journal of environmental research and public health · 2021Pooled it
- Artificial Intelligence for Clinical Decision Support in Rural Spine Care: A Narrative Review.Healthcare (Basel, Switzerland) · 2026Review
- A meta-model of low back pain to examine collective expert knowledge of treatment effects and their mechanisms.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- Artificial intelligence-based prognostic modeling in temporomandibular disorders and chronic orofacial pain: a critical conceptual review.Journal of oral & facial pain and headache · 2026Review
- Artificial Intelligence in Neuropathic Pain: From Mechanisms to Neuromodulation and Regenerative Strategies.Current pain and headache reports · 2026Review
- Open-Source Large Language Models and AI Health Equity: A Health Service Triangle Model Perspective.Journal of medical Internet research · 2026Article
- Machine Learning-Based Pattern Recognition of Risk Factors for Low Back Pain among Adolescent Cricket Players in Dhaka City.PLOS digital health · 2026Article
- Artificial intelligence in spine care: A scoping review of treatment applications.North American Spine Society journal · 2026Review
- Machine learning models for predicting treatment outcomes in chronic non-specific back pain patients undergoing lumbar extension traction.Scientific reports · 2026Article
- The Clinical Significance of the Manchester Colour Wheel in a Sample of People Treated for Insured Injuries.Journal of clinical medicine · 2025Article
- A meta-model of low back pain to examine collective expert knowledge of the effects of treatments and their mechanisms.Research square · 2025Article
- Exploring the role of artificial intelligence in orthopedic medical education: A narrative review.Journal of clinical orthopaedics and trauma · 2025Article
- Bioengineering Support in the Assessment and Rehabilitation of Low Back Pain.Bioengineering (Basel, Switzerland) · 2025Review
- Explainable AI reveals tissue pathology and psychosocial drivers of opioid prescription for non-specific chronic low back pain.Scientific reports · 2025Article
- Using graph machine learning to identify functioning in patients with low back pain in terms of ICF.Scientific reports · 2025Article
- Integrating multidimensional data analytics for precision diagnosis of chronic low back pain.Scientific reports · 2025Article
- Low Back Pain Among Health Sciences Undergraduates: Results Obtained from a Machine-Learning Analysis.Journal of clinical medicine · 2025Article
- Advancing Medical Research Through Artificial Intelligence: Progressive and Transformative Strategies: A Literature Review.Health science reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence and machine learning (AI/ML) could enhance the ability to detect patterns of clinical characteristics in low-back pain (LBP) and guide treatment. We conducted three systematic reviews to address the following aims: (a) review the status of AI/ML research in LBP, (b) compare its status to that of two established LBP classification systems (STarT Back, McKenzie). AI/ML in LBP is in its infancy: 45 of 48 studies assessed sample sizes <1000 people, 19 of 48 studies used ≤5 parameters in models, 13 of 48 studies applied multiple models and attained high accuracy, 25 of 48 studies assessed the binary classification of LBP versus no-LBP only. Beyond the 48 studies using AI/ML for LBP classification, no studies examined use of AI/ML in prognosis prediction of specific sub-groups, and AI/ML techniques are yet to be implemented in guiding LBP treatment. In contrast, the STarT Back tool has been assessed for internal consistency, test-retest reliability, validity, pain and disability prognosis, and influence on pain and disability treatment outcomes. McKenzie has been assessed for inter- and intra-tester reliability, prognosis, and impact on pain and disability outcomes relative to other treatments. For AI/ML methods to contribute to the refinement of LBP (sub-)classification and guide treatment allocation, large data sets containing known and exploratory clinical features should be examined. There is also a need to establish reliability, validity, and prognostic capacity of AI/ML techniques in LBP as well as its ability to inform treatment allocation for improved patient outcomes and/or reduced healthcare costs.
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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.