## 4 Discussion
### Assessment of BiomedCLIP Accuracy in Classifying Sociosis Severity  
The results of the BiomedCLIP model in predicting scoliosis severity showed varying effectiveness depending on the disease stage. For mild scoliosis, the model achieved moderate accuracy (0.64), with a sensitivity of 0.79 and specificity of 0.61, highlighting limitations in precision. For moderate scoliosis, accuracy improved slightly to 0.70, with sensitivity at 0.74 and specificity at 0.68, indicating balanced performance in identifying true cases and reducing false positives. The best results were observed for severe scoliosis, where accuracy reached 0.80, sensitivity 0.84, and specificity 0.77, reflecting strong effectiveness in recognizing advanced cases.  
The AUC analysis corroborates these findings. For mild scoliosis, the AUC was 0.75, suggesting moderate discrimination ability, while for moderate scoliosis, it was 0.74. For severe scoliosis, the AUC peaked at 0.87, underscoring excellent detection capability. The ROC curves further illustrate that the model consistently performed better than random guessing, particularly excelling in severe cases. These results indicate that BiomedCLIP performed significantly better in detecting severe scoliosis compared to mild and moderate stages. The model's stronger performance in advanced cases can be attributed to more pronounced physiological changes visible in imaging. In contrast, subtler changes in mild scoliosis presented greater challenges, especially in maintaining specificity. The difficulty in detecting mild scoliosis reflects the subtleties of the disease, which make it harder for the model to discern changes on radiographic images. Hypothesis (H3), which proposed that BiomedCLIP would accurately classify scoliosis severity, was only partially confirmed. Hypothesis H3 proposed that BiomedCLIP would accurately categorize the severity of scoliosis into mild, moderate, and severe stages. The results indicate that the model's performance varies depending on the severity stage. For severe scoliosis, BiomedCLIP achieved strong predictive power, with an accuracy of 0.80, sensitivity of 0.84, and specificity of 0.77, supported by an AUC of 0.87. These results confirm the model's robustness in detecting advanced scoliosis cases, likely due to the more pronounced radiographic markers in severe stages.  
In comparison, the model's performance was more moderate for mild and moderate scoliosis. For mild cases, sensitivity reached 0.79, but accuracy was lower at 0.64, and specificity was limited to 0.61, indicating a tendency for false positives. Similarly, moderate cases showed a balanced performance with an accuracy of 0.70, sensitivity of 0.74, and specificity of 0.68, yet still fell short of the hypothesis's expectation for high predictive precision. These findings highlight that while BiomedCLIP demonstrates notable success in identifying severe scoliosis, its ability to reliably classify mild and moderate stages remains limited.  
The observed variability underscores the need for further refinement of the model and expansion of training datasets. Including more annotated examples of mild and moderate scoliosis, as well as enhancing image preprocessing techniques, could improve performance across all severity stages. Advanced AI models, although promising, still face limitations in accurately assessing Cobb angles and classifying less advanced cases of scoliosis [33]. Despite these advancements, the challenges of diagnosing mild scoliosis emphasize the need for further refinement of AI models to enhance diagnostic capabilities across all severity stages.  
In conclusion, the hypothesis regarding the model's ability to classify scoliosis severity was only partially confirmed. BiomedCLIP demonstrated strong performance in detecting severe scoliosis but moderate effectiveness for mild and moderate cases. Further optimization is necessary to improve detection in early and intermediate stages of scoliosis.