ReviewBMJ health & care informatics2026
Artificial intelligence in lumbar radiography: bridging deep learning and clinical practice in low-resource environments.
Review in BMJ health & care informatics, 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
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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
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) has increasingly been applied to medical imaging, yet its role in lumbar spine radiography, particularly in low-resource settings, remains underexplored.
objectiveTo evaluate recent developments in AI-based approaches for lumbar spine radiography and their clinical applicability in resource-constrained environments.
methodsA narrative review was conducted focusing on deep learning models applied to lumbar radiographic analysis. Studies published between 2022 and 2024 were identified through structured screening of PubMed and Google Scholar.
resultsDeep learning models, including convolutional neural networks, U-Net, ResNet and generative adversarial networks, have demonstrated improved performance in segmentation, classification and curvature analysis. Lightweight architectures show potential for deployment in resource-limited settings.
conclusionAI-based lumbar imaging has the potential to enhance diagnostic accuracy and workflow efficiency in low-resource environments. However, challenges related to validation, interpretability and clinical integration remain, highlighting the need for further large-scale and real-world studies.
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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 Socratic graph.