## 4 Discussion
### Performance of BiomedCLIP in Detecting Single-Curve and Double-Curve Scoliosis  
The results of the BiomedCLIP model demonstrated limited effectiveness in predicting both single-curve and double-curve scoliosis. For single-curve scoliosis, the model achieved low accuracy (0.53) and sensitivity (0.35), indicating difficulty in detecting actual cases. Although specificity was higher (0.78), the model struggled to confirm the presence of single-curve scoliosis, with an AUC of 0.53 suggesting performance only slightly better than random guessing. For double-curve scoliosis, the model showed a reverse pattern: higher sensitivity (0.78) but low specificity (0.35), resulting in numerous false positives. Accuracy remained at 0.53, and the AUC again indicated near-random performance (0.53). These consistent AUC results underscore the model's challenges in distinguishing between single-curve and double-curve scoliosis The hypothesis (H2) that BiomedCLIP would accurately identify scoliosis types with high sensitivity was not fully confirmed. Hypothesis H2 posited that BiomedCLIP would accurately identify scoliosis types (single-curve and double-curve) with high sensitivity. However, the results demonstrated mixed outcomes. The model showed significant challenges in predicting single-curve scoliosis, achieving a sensitivity of only 0.35 and an AUC of 0.53, indicating performance slightly above random guessing. While the model exhibited higher specificity (0.78) in this category, it frequently failed to detect positive cases, leading to low overall accuracy (0.53). This suggests that the subtle radiographic features of single-curve scoliosis present a greater challenge for the model, likely due to the limited visibility of curvature in certain mild or intermediate stages. The overlapping of the two ROC curves near the middle of the sensitivity range reflects the complementary nature of the probabilities assessed by the AI model for features with two categories (single and double scoliosis). This behavior suggests that the model evaluates the features in a way that treats the two scoliosis types as interdependent or mutually exclusive. This indicates that the AI model is likely assigning probabilities based on a shared feature set, where the sum of probabilities for both categories equals 1. The complementary probabilities reflect a balancing act by the model, where it adjusts predictions for one category as the likelihood for the other category increases. This approach is common in binary classification tasks, particularly when the model is trained on features that do not strongly distinguish between the two categories. This complementary relationship in probability assessment is reflected in the nearly identical AUC values (0.53 for both single and double scoliosis) and the overlapping confidence intervals (CI: 0.46-0.60). These metrics suggest that the model's performance in distinguishing between the two categories is limited and heavily influenced by shared characteristics of the feature space. In contrast, the model performed better in detecting double-curve scoliosis, achieving a sensitivity of 0.78. However, this improvement came at the cost of reduced specificity (0.35), with the model misclassifying a notable number of non-double-curve cases as positive. The AUC value of 0.53 again highlights the lack of strong predictive capability. These results collectively suggest that while BiomedCLIP is capable of identifying double-curve scoliosis more reliably than single-curve cases, its overall performance in distinguishing scoliosis types remains limited. Further optimization, such as introducing additional training data with well-annotated examples of scoliosis types, is necessary to address these challenges. Low sensitivity for single-curve scoliosis and low specificity for double-curve scoliosis suggest that the model frequently failed to identify single-curve cases while misclassifying many as double-curve.