The review identifies three core challenges that have defined traditional pain management for decades. First, assessment depends on patients describing their own suffering, a process vulnerable to emotional state, cultural background and cognitive function, and effectively unusable for infants, dementia patients, the critically ill and postoperative patients who cannot self-report. Second, conventional imaging lacks the sensitivity to detect many pain-related structural changes: plain X-rays miss early osteoarthritis and soft tissue lesions, while MRI, despite excellent soft tissue resolution, struggles with functional pain and is expensive and time-consuming. Third, treatment selection relies on clinical experience and guidelines without individualized prediction, leaving roughly 30 to 40 percent of patients failing to respond adequately to their initial regimen. The result is prolonged suffering, repeated medication adjustments, rising costs and heightened risk of adverse drug reactions. Deep learning, the authors contend, offers an end-to-end pathway from symptom identification to mechanism analysis, extracting latent pain biomarkers from multi-source heterogeneous data.

The most technically rich portion of the review concerns objective pain assessment, where deep neural networks are being trained to quantify suffering from signals that patients cannot suppress. Computer vision models analyze facial micro-expressions such as frowning and squinting; speech systems capture changes in vocal tone, pitch jitter and spectral energy; and physiological pipelines integrate electroencephalography, skin conductance, heart rate variability and respiration. The performance figures are striking. A neonatal convolutional neural network recognizing pain from infant facial expressions achieved 91 percent accuracy with an area under the curve of 0.93, while a three-branch network analyzing newborn cries reached 96.77 percent accuracy in binary classification, using only about 2.6 percent of the parameters of VGG16. In adults, a spatial-temporal attention LSTM network classified postoperative pain into three levels from facial landmarks with 86.6 percent accuracy, and an autoencoder-LSTM model fused motion capture with surface electromyography to detect protective behaviors, improving over single-modality baselines by 38.5 percent.

Physiological signals have proven equally fertile ground. A framework called PainAttnNet, built on transformer architectures with multiscale feature extraction, classified pain intensity from electrodermal activity with 85.56 percent accuracy on the BioVid dataset. A dual-branch spatiotemporal model processing scalp EEG in children distinguished pain from non-pain states with 87.83 percent accuracy, and, notably, visualization of electrode contributions showed that accuracy remained at 84 percent even when only nine electrodes were retained, a finding that could dramatically simplify data collection in pediatric settings. A hybrid BiLSTM-support vector machine pipeline classified postoperative pain intensity from electrocardiographic signals at 84.14 percent validation accuracy, while bidirectional LSTMs applied to functional near-infrared spectroscopy achieved 90.6 percent accuracy across four pain intensity categories, outperforming unidirectional variants by 5 to 8.4 percentage points. Resting-state frontal EEG biomarkers have likewise been proposed for grading chronic neuropathic pain severity, moving the field closer to objective clinical translation.

Multimodal fusion, however, emerges as both the field’s greatest promise and its most sobering cautionary tale. Because any single signal can be lost in real clinical environments, obscured by oxygen masks, sedation, motion artifacts or equipment failure, fusing facial, vocal and physiological streams offers redundancy and robustness. In neonatal postoperative pain assessment, a decision-level voting fusion of facial expressions, body movements and crying maintained strong performance even when a quarter of each modality’s data was randomly removed, with the fused area under the curve of 0.868 clearly surpassing the best single modality at 0.774. Yet the review is candid that fusion is not a universal win: in real postoperative wards, single-modality models, particularly those using respiratory rate at 88.24 percent balanced accuracy, consistently outperformed multimodal fusion, which was degraded by motion artifacts, asynchronous acquisition and environmental noise rarely encountered in laboratory datasets. The authors call for cross-modal pretraining, medical knowledge graphs and event-driven fusion strategies to close this gap.

The second pillar of the review concerns intelligent structural identification, where convolutional and transformer-based models automatically segment the anatomical landscape of pain. Deep learning systems now detect lumbar spondylolisthesis from X-rays, quantify vertebral fractures through anchor-free keypoint detection with expert-level localization error of 0.92 millimeters and an AUC of 0.96, and grade intervertebral disc degeneration from MRI in real time using YOLOv5 architectures with over 95 percent classification accuracy. On the cervical spine, where vertebral similarity and complex anatomy make segmentation notoriously difficult, a 2D U-Net framework with superior-inferior labeling achieved Dice coefficients above 94 percent even on pathological data, and a transformer-based model reduced radiologist interpretation time for degenerative cervical MRI from up to 490 seconds to as little as 90 seconds, with the greatest benefit accruing to residents.

Nerve and needle localization extend this vision into interventional precision. Mask R-CNN-based systems segment the median nerve at the carpal tunnel from ultrasound without manual region selection, while U-Net variants track the vagus nerve in real time with over 90 percent recognition accuracy even in low-quality images, trained from mere bounding-box annotations. The dorsal root ganglion, a structure implicated in neuropathic pain but historically too small to segment automatically, has now been delineated in MRI using a meta-optimized nnU-Net framework, revealing genotype-related volume changes in a Fabry disease model. For ultrasound-guided nerve blocks, deep networks locate needle tips that are frequently invisible at steep angles: time-aware LSTMs combined with dynamic background subtraction recover weak tip echoes, and an optical-flow-enhanced YOLO variant tracks speckle dynamics of entirely invisible needles while cutting model parameters by 98 percent for real-time deployment, reaching sub-millimeter localization accuracy in robotic settings.

The third pillar maps AI onto specific pain conditions. For shoulder disorders, multimodal models fusing X-rays with clinical data rule out rotator cuff tears with 97.3 percent sensitivity, and 3D networks trained on more than 11,000 MRI studies classify full-thickness tears with AUCs as high as 0.99, outperforming experienced radiologists. In osteoarthritis, deep stacked ensembles grade knee severity at up to 99.71 percent accuracy, automated systems measure hip-knee-ankle angles 126.7 times faster than manual workflows, and a model called DeepKOA predicts structural and symptomatic progression over 24 to 48 months from multimodal MRI. Multiomic deep clustering has even identified three molecular subtypes of knee osteoarthritis that predict post-arthroplasty pain outcomes with AUCs of 0.84 to 0.88. For trigeminal neuralgia, machine learning on brain morphology predicted gamma knife surgery efficacy with 96.7 percent accuracy, and radiomics models now identify which patients will achieve durable relief from percutaneous balloon compression, lifting three-year pain-free survival in favorable subgroups from 51.1 percent to 86.4 percent. Machine learning models predicting postherpetic neuralgia from 23,326 real-world electronic health records, and LSTM networks forecasting cancer pain exacerbations hours before onset, illustrate the shift toward preemptive intervention.

The review closes with a bracing reality check. Most studies remain small, single-center and internally validated; when tested externally, performance routinely collapses, as when a sacroiliitis model’s sensitivity plummeted from near-expert levels to 56 percent. Data imbalance, annotation inconsistency, demographic bias and unmodeled anatomical variation pervade the literature, and explainable AI outputs often misalign with what clinicians actually need. Privacy risks from biometric pain data, unresolved liability frameworks, absent reimbursement mechanisms and the economic burden of deployment all stand between laboratory success and bedside reality. The authors argue that pain AI must now pivot from a method-driven race for benchmark accuracy to an evaluation-driven, utility-driven paradigm in which human-AI collaboration, longitudinal outcomes and patient-centered benefit define success. If that transition succeeds, the era in which suffering could only be described, rather than measured, understood and preempted, may finally be drawing to a close.

Subject of Research: Artificial intelligence applications in objective pain assessment, medical image analysis and treatment decision-making for chronic pain conditions

Article Title: Current state of research and future developments of artificial intelligence in pain diagnosis and treatment

Article References: Current state of research and future developments of artificial intelligence in pain diagnosis and treatment. (n.d.). https://doi.org/10.1186/s12967-026-08529-9

Image Credits: AI Generated

DOI: 10.1186/s12967-026-08529-9

Keywords: artificial intelligence, chronic pain, deep learning, pain assessment, multimodal data fusion, medical imaging, trigeminal neuralgia, osteoarthritis, postherpetic neuralgia, cancer pain, explainable AI, precision medicine