Limitations of Parameter-Based AI Models in the MRI Evaluation of Lumbar Spinal Stenosis: A Clinical Perspective

AbstractBackground: Lumbar spinal stenosis (LSS) is a prevalent degenerative spinal disorder in neurosurgical practice, where magnetic resonance imaging (MRI) plays a central role in diagnosis and surgical planning. In recent years, artificial intelligence (AI), particularly deep learning–based models, has shown promising results in automated MRI analysis, including stenosis grading and segmentation. However, most existing AI systems rely heavily on predefined quantitative parameters, raising concerns regarding their clinical applicability.Objective: This study aims to critically evaluate the limitations of parameter-based AI models in the MRI assessment of LSS and to propose a conceptual framework for clinically aligned, explainable hybrid AI approaches.Methods: A narrative and conceptual analysis was conducted based on current literature and clinical experience. The limitations of parameter-based AI models were examined in terms of standardization, clinical decision-making complexity, clinicoradiological mismatch, multilevel disease evaluation, imposed parameterization, and labeling variability.Results: Parameter-based AI models demonstrate important limitations in reflecting real-world clinical decision-making. The lack of standardized thresholds, inability to integrate multidimensional clinical factors, and challenges in identifying symptomatic levels in multilevel disease reduce their clinical utility. Additionally, interobserver variability in labeling introduces noise that negatively affects model performance and generalizability.Conclusion: While parameter-based AI models contribute to the quantitative evaluation of LSS, they remain insufficient for comprehensive clinical decision support. Explainable hybrid AI approaches that integrate imaging data with clinical variables and structured knowledge representations may offer a more reliable and clinically meaningful framework for future applications.