Integrating predictive modeling, imaging, and EHR-linked decision support in spine informatics

Artificial intelligence (AI) is increasingly transforming spine care by enabling analysis of imaging, clinical, and textual data to support diagnosis, risk stratification, and treatment planning. Techniques including deep learning-based image analysis, radiomics, and natural language processing (NLP) offer opportunities to standardize interpretation and derive quantitative biomarkers beyond human visual assessment. This narrative review examines recent innovations in spine-focused biomedical informatics and evaluates their clinical relevance and translational challenges.

A narrative review was conducted, focusing on AI in spine care across imaging, radiomics, NLP of electronic health records, and clinical decision support. Literature was selected via targeted PubMed searches; studies describing technical development, validation, and clinical implementation were synthesized, addressing explainability, uncertainty, workflow, and regulation.

Convolutional neural networks enable vertebral and disc segmentation, detection of degenerative and traumatic pathology, and quantitative assessment of paraspinal musculature. Radiomics approaches extract high-dimensional imaging features that improve the prediction of osteoporosis, fracture risk, tumor characterization, and surgical outcomes. NLP techniques, including emerging large language models, unlock clinically relevant information from unstructured text, enhancing outcome prediction and population-level analyses. Multimodal clinical decision support systems integrating imaging, EHR data, and NLP-derived features demonstrate promise for personalized risk assessment and treatment guidance. Challenges remain regarding data heterogeneity, generalizability, privacy, and prospective validation.

AI-driven biomedical informatics holds substantial promise to enhance diagnostic accuracy, prognostication, and personalized decision-making in spine care. Successful clinical translation will require robust validation, explainable, uncertainty-aware models, regulatory alignment, and integration into clinical workflows to ensure safe, trustworthy, and patient-centered adoption.