## 4 Discussion
### Challenges with False Features  
Research indicates that models like CLIP can learn false correlations, reducing performance when data distribution changes [36]. Relying on such false features may limit effectiveness in scenarios with obvious but less specific changes, as the model may misinterpret signals. However, while data diversity poses challenges, it also broadens the model's learning context, potentially increasing robustness in real-world applications. Comparing the results of CLIP and BiomedCLIP is not entirely fair, as they were tested under different conditions. CLIP was evaluated on a simple task--detecting severe single-curve scoliosis--and performed well without specialized training. BiomedCLIP, on the other hand, had to handle a wider range of cases, including differentiating scoliosis types and severities, introducing complexities that likely impacted its performance. The differing test scenarios underline that the results of these models are not directly comparable. CLIP's strong performance stemmed from a simple, focused task, while BiomedCLIP's specialization required it to navigate more challenging diagnostic cases, affecting its accuracy in severe scoliosis detection. The performance of BiomedCLIP can also be partially attributed to limitations in the dataset used for training. The PMC-15M dataset, containing 15 million image-text pairs from PubMed Central, includes a wide variety of biomedical image types, such as microscopy and histology. However, it does not guarantee suitability for scoliosis-specific training. A 2023 PubMed search for \"scoliosis\" yielded only approximately 31,226 articles, representing a small fraction of the PMC database. Many of these articles may lack relevant X-ray images, focusing instead on genetic, molecular, or clinical studies. Limited access to appropriate clinical scoliosis images likely constrained BiomedCLIP's ability to detect mild cases and differentiate curvature types effectively. BiomedCLIP's use of the ViT (Vision Transformer) architecture also presents challenges for medical data. Transformers, initially designed for large-scale text data, require extensive datasets to extract meaningful patterns. Medical data, by contrast, are often highly specialized and context-dependent. Limited, variable datasets make it difficult to identify consistent patterns, leading to potential inaccuracies in predictions. Additionally, the complexity of interpreting medical images, which often depends on expert context, adds further difficulty. These limitations underscore the need for more targeted datasets and tailored architectural adjustments to improve performance in medical imaging tasks. A significant challenge arises from the use of contrastive learning, a major component of transformer architectures, including BiomedCLIP. Contrastive learning can exacerbate these misinterpretations, potentially leading to incorrect classifications. Moreover, the limited availability of data presents another obstacle. The creators of BiomedCLIP themselves acknowledged that small datasets are insufficient for fine-tuning models like CLIP [34]. For instance, PubMedCLIP was trained on the ROCO dataset, which contains 81,000 images [37]. However, in the case of scoliosis, there are far fewer relevant articles and even fewer high-quality, annotated scoliosis images, further restricting the available data for model training. This leads to another issue with the PMC-15 dataset, which focuses primarily on images where the accompanying text consists mainly of annotations. These annotations can be misleading and may provide incorrect clues about the overall context. Additionally,many medical conditions require a highly specific set of parameters for diagnosis, making it difficult to classify them correctly without sufficient and accurate data. Another limitation arises from the structure of the dataset itself, which includes images and descriptions from a wide range of biomedical fields.