Table 5

Deep learning is emerging as a transformative tool for chronic pain research. In this systematic review, we evaluated deep learning applications across diverse data modalities (neuroimaging, electrophysiological signals, motion capture, and wearable sensors) for chronic pain diagnosis, classification, and prognostication. A comprehensive search of seven electronic databases identified 22 eligible studies using convolutional neural networks, recurrent neural networks, and hybrid models. Study quality was appraised using PROBAST and PRISMA guidelines. While these studies demonstrate the potential to overcome limitations of subjective pain assessments, heterogeneity in methodologies, sample sizes, and evaluation metrics precluded meta-analysis. Key challenges include data imbalance, limited external validation, and variability in preprocessing approaches. Despite these limitations, deep learning generates non-invasive biomarkers and supports precision medicine strategies in chronic pain management, potentially improving clinical decision-making and reducing the societal burden of chronic pain.