Latest AI and machine learning research in pain management for healthcare professionals.
BACKGROUND: Large language models (LLMs) are rapidly evolving from text-based agents to multimodal systems capable of interpreting medical images. While their textual reasoning has improved, the safety implications of this shift remain underexplored, specifically regarding the alignment between visual interpretation and textual advice in low back pain (LBP) management. OBJECTIVE: This study aims t...
OBJECTIVES: Accurate radiographic identification of dental implant systems is essential for effective clinical management; however, manual assessment remains time-consuming and susceptible to diagnostic error. Furthermore, conventional computational approaches are constrained by their dependency on manually annotated regions of interest. To overcome these limitations, this study introduces DentalI...
BACKGROUND: Sleep supports neurophysiological maturation in preterm infants, yet the impact of developmental care interventions on sleep remains poorl...
Existing deep-learning-based sleep staging frameworks frequently rely on shared multimodal feature extractors, which may overlook modality-specific di...
BACKGROUND: Multiple sclerosis (MS) is increasingly recognised as a disorder of large-scale brain network reorganisation rather than a disease explain...
The growing recognition of the human microbiome as a key regulator of immune homeostasis has accelerated the application of computational intelligence...
AIM: To investigate whether artificial intelligence (AI) models trained on standard 12-lead electrocardiograms (ECG) can identify symptom-defined diab...
The retinal ganglion cell layer integrates and transmits stimuli from photoreceptors to the central nervous system. Retinal ganglion cell loss is a ha...
Intervertebral disc degeneration (IVDD) is a major contributor to low back pain, but the immune-metabolic events that sustain disc inflammation remain...
OBJECTIVES: We aimed to forecast headache in individuals with persisting postconcussion symptoms using foundation machine learning (ML) models and mul...
To evaluate the diagnostic performance of deep learning-derived left ventricular circumferential strain (DL-LVCS), a method that reduces operator depe...
BACKGROUND AND OBJECTIVES: Accurately identifying candidates likely to benefit from surgery and addressing modifiable preoperative risk factors are ce...
OBJECTIVE: The accuracy of psychological stress detection hinges upon the precision with which models capture complex and variable physiological respo...
INTRODUCTION: Long-term sickness absence represents a major public health challenge with far-reaching consequences for both individuals and society. M...
PURPOSE: Adult degenerative scoliosis arises after skeletal maturity in an initially normal spine, primarily driven by age-related degeneration. The C...
Advances in image registration and machine learning have recently enabled volumetric analysis of postmortem brain tissue from conventional photographs...
Drug- and metal-induced liver damage (DILI/MILI) continues to be a predominant cause of acute hepatic failure globally, with two clinically significan...
BACKGROUND AND AIM: Artificial Intelligence (AI) has been increasingly used in cancer survivorship to support symptom management. This scoping review ...
PURPOSE: Lumbar spinal stenosis (LSS) is a common degenerative spinal condition and a leading cause of pain and disability in adults. With increasing ...
BACKGROUND: Large language models (LLMs) have emerged as powerful transformer-based systems capable of capturing long-range dependencies and complex s...