Artificial Intelligence in Spinal Diagnostics: Advances, Challenges and Future Directions

Artificial Intelligence (AI) is increasingly used in spinal diagnostics, particularly through machine learning and deep learning applied to medical imaging and clinical data. These technologies may improve the detection and classification of spinal disorders, automate image interpretation and support clinical decision-making. This narrative review summarizes current applications of AI in spinal diagnostics, focusing on methods for detecting spinal cord injuries, spinal tumors, degenerative spine diseases and vertebral fractures, as well as predicting treatment outcomes and postoperative complications. It also examines convolutional and recurrent neural networks, automated image segmentation and other machine learning approaches for analyzing spinal images and clinical information. Available evidence suggests that AI models can perform comparably to conventional methods and expert assessments and may exceed them in some settings. However, implementation remains limited by challenges related to dataset quality and diversity, model generalizability and interpretability, data privacy and regulatory and ethical considerations. This review highlights both the clinical potential of AI and the major barriers to its integration into routine spinal diagnostic practice. Further prospective validation, external testing across diverse patient populations and interdisciplinary collaboration will be essential to establish the clinical utility and ensure the safe implementation of AI-based spinal diagnostic tools.