Integrating Multi-Modal Predictive Modeling, Imaging Biomarkers, and EHR-Linked Decision Support Systems in Spine-Focused Biomedical Informatics

Abstract

Spine-focused biomedical informatics has emerged as a critical frontier for applying artificial intelligence (AI) to improve diagnosis, prognosis, and intervention in complex musculoskeletal and neurodegenerative conditions. This review synthesizes recent developments in the integration of machine learning (ML), deep learning (DL), and natural language processing (NLP) within spine health ecosystems. We highlight convolutional neural networks for vertebral segmentation and disc classification, radiomic pipelines for quantitative MRI/CT biomarker extraction, and multimodal ML models for outcome prediction across degenerative, neoplastic, and traumatic pathologies. We examine spine-specific clinical decision support systems (CDSSs) that fuse structured EHR data with NLP-parsed reports, wearable biomechanics, and genomic signals to generate real-time, risk-adaptive treatment guidance. Emphasis is placed on explainable AI (XAI) frameworks—e.g., SHAP, LIME, Grad-CAM—that support interpretability and ethical accountability in AI-augmented decision-making. We analyze challenges in generalizability, privacy-preserving federated learning, and regulatory heterogeneity, with attention to evolving U.S. FDA guidance, EU AI Act obligations, and liability frameworks. Integrating epistemic transparency with computational scalability, this review offers a roadmap for translating AI-enhanced spine informatics into safe, equitable, and clinically trustworthy systems—advancing the goals of biomedical informatics by unifying predictive analytics with ethical and patient-centered care.

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