Advanced imaging technologies have revolutionized the diagnosis and management of spinal pathologies by providing superior precision and efficiency. Modalities such as PET-CT, SPECT, diffusion tensor imaging (DTI), and magnetic resonance spectroscopy (MRS) offer unique insights into the metabolic, structural, and functional aspects of spinal diseases, enabling better differentiation of lesions, improved surgical planning, and early detection of pathological changes. Furthermore, the integration of artificial intelligence (AI) has enhanced imaging workflows by enabling automated analysis, prediction of clinical outcomes, and segmentation of spinal structures. Despite these advancements, challenges such as technical limitations, high costs, and ethical concerns, including issues of data privacy and AI-generated inaccuracies, hinder widespread adoption. This review explores the clinical applications, limitations, and future directions of these emerging technologies, highlighting the need for multidisciplinary collaboration and large-scale research to standardize protocols and optimize patient outcomes. The seamless integration of advanced imaging and AI represents a transformative potential for improving diagnostic accuracy and treatment efficacy in spinal care.
