## 4 Discussion
### Challenges with False Features
This diversity can result in the model being overly generalized in its predictions, making it less accurate for specific conditions like scoliosis. Furthermore, the lack of open-access PubMed articles restricts access to complete imaging datasets, which may have affected the quality and diversity of the training data available for BiomedCLIP. It is also important to note that our analysis was limited to radiographic images, which could restrict the generalizability of the results to other imaging techniques, such as MRI or CT. The lack of relevant data from other biomedical imaging modalities may have further constrained the model's effectiveness in the broader diagnostic context of scoliosis. Recent advancements in deep learning (DL) have significantly improved scoliosis detection and quantification, particularly in tasks such as axial vertebral rotation (AVR) estimation and Cobb angle (CA) measurement. For example, [PERSON] et al. proposed an automatic AVR measurement method that combines verteba landmark detection and pedicle segmentation using an improved High-Resolution Network (HR-Net) with CoordConv layers and self-attention mechanisms. Their approach demonstrated high accuracy in estimating AVR by precisely extracting pedicle center coordinates and vertebral landmarks. This landmark-based approach provides detailed structural information, which is critical for assessing three-dimensional spinal deformities, particularly in adolescent idiopathic scoliosis (AIS) [38]. Similarly, a recent systematic review of DL methods for CA measurement highlights various approaches, such as U-Net variants, Capsule Neural Networks (CapsNets), and multi-stage pipelines, achieving notable accuracy in estimating spinal curvature. For instance, CapsNet achieved a Pearson correlation coefficient (PCC) of 0.93, while LocNet and RegNet models demonstrated robust vertebral localization and CA prediction performance. These approaches rely on precise manual annotations of vertebral landmarks and demonstrate strong capabilities in quantifying curvature, which remains a gold standard for scoliosis severity assessment [39]. Our study differs from these methods by addressing scoliosis classification tasks, specifically severity stages (mild, moderate, severe) and curvature types (single-curve, double-curve), using the BiomedCLIP model. Unlike landmark-dependent AVR or CA measurement methods, BiomedCLIP leverages a contrastive learning framework to embed image-text relationships, enabling classification without requiring explicit anatomical markers or angle calculations. This approach is particularly advantageous for automating scoliosis triage and broad classification in clinical settings. However, similar challenges are observed across studies, including our own. While AVR- and CA-based methods achieve high precision in quantifying spinal deformities, they often require extensive manual annotations and are sensitive to image quality and data variability. Our findings, such as the lower sensitivity for mild scoliosis and reduced specificity for double-curve detection, align with these limitations. Moreover, subtle imaging features in early-stage scoliosis remain a significant challenge for both CA-based measurement and classification approaches. Integrating precise AVR and CA measurement techniques with models like BiomedCLIP represents a promising direction for future research. By combining detailed anatomical quantification with classification-based predictions, a more comprehensive AI-driven framework could be developed, enhancing scoliosis diagnosis, severity assessment, and treatment planning. This suggests the need for further optimization of the model, as discussed earlier, to improve its sensitivity in detecting subtle cases of scoliosis and accurately differentiate curvature types. Integrating precise AVR and CA estimation techniques with BiomedCLIP's classification framework could be a valuable direction for future research.