## 1 Introduction
With its advanced technology, BiomedCLIP is becoming a powerful tool supporting diagnostics, scientific research, and medical education, opening new possibilities in the analysis and understanding of complex biomedical information [9]. Although BiomCLIP demonstrates superior performance across most evaluated benchmarks, Figure 1 highlights an exception. In the PCam benchmark, PubMedCLIP outperformed BiomedCLIP, showcasing higher accuracy in this specific task. This observation suggests that while BiomedCLIP is a robust and versatile model, certain domain-specific datasets may still favor more specialized approaches like PubMedCLIP. This underscores the need for continued research to optimize vision-language models for diverse biomedical tasks. Previous studies utilized the raw version of the CLIP model to evaluate posturographic X-ray images in the anteroposterior projection, demonstrating single-curve scoliosis at a severe stage. Since CLIP offers nine models with different architectures, our study partially confirmed the initial research hypothesis, indicating that only seven out of nine tested CLIP models were able to effectively recognize scoliosis from radiographic images. The second hypothesis was not confirmed, as only four of the nine models correctly answered questions regarding single-curve scoliosis. Assumptions regarding the accurate estimation of curvature degrees using AI were also not confirmed--none of the selected models achieved high sensitivity in the assessment of Cobb angles [10]. Scoliosis is a complex, three-dimensional deformity of the spinal column, identified by a curvature greater than 10 degrees, as determined by the Cobb method [11]. The diagnosis of scoliosis typically involves a comprehensive radiological posturographic examination of the entire spine in both anterior-posterior (AP) and lateral views, which facilitates the assessment of curvature, vertebral rotation, spinal dynamics, and trunk deformity, and aids in planning appropriate therapy [3; 4]. Scoliosis is classified based on factors such as the patient's age, degree of curvature, underlying cause, location, and number of curves present. In this study, scoliosis is categorized into single-curve (C-shaped) and double-curve (S-shaped) forms [5]. The severity of scoliosis, as indicated by the Cobb angle, is classified into mild (10-20 degrees), moderate (20-40 degrees), and severe (greater than 40 degrees) [5; 12] (Figure 2). Figure 1: The creators uploaded the reconstructed figure onto the website, providing a visual representation of the performance comparison of BiomedCLIP, PubMedCLIP, and CLIP models across various QA benchmarks [9]. The results demonstrate that BiomedCLIP consistently outperforms the other models, particularly in tasks related to text-image retrieval, zero-shot image classification, and visual question answering (VQA). This study aimed to assess how a dedicated medical AI model, such as BiomedCLIP, performs in analyzing posturographic X-ray images that display both single-curve and double-curve scoliosis. To investigate this, three sets of questions were developed, partially building on prior research. The questions addressed whether the model detects the presence of scoliosis, correctly identifies its single-curve or double-curve form, and evaluates the severity of scoliosis--categorized as mild, moderate, or severe. Questions regarding Cobb angles were deliberately excluded, as earlier studies indicated that the CLIP model struggles with accurately determining these angles, often performing this task randomly.