## 1 Introduction  
Open-source artificial intelligence models (OSAIMs) are free, publicly available tools that have found applications in various fields, including computer science and medicine [1, 2]. While OSAIMs offer advanced capabilities for processing and interpreting visual data, most of them are not specifically tailored for medical image analysis, such as X-ray images. In response to this gap, dedicated models like BiomedCLIP have been developed, combining natural language processing with medical image analysis, providing new opportunities for the evaluation and interpretation of radiological data. The evaluation of scoliosis, a complex, three-dimensional deformity of the spinal column, traditionally relies on manual radiological interpretation by clinicians. This process includes the assessment of curvature, vertebral rotation, and spinal dynamics, typically using X-ray images in anterior-posterior (AP) and lateral views [3; 4]. Identifying the degree and type of scoliosis requires significant expertise and can be time-consuming, especially when differentiating between single-curve and double-curve forms or determining the severity based on the Cobb angle [5]. Challenges such as interobserver variability and the high volume of radiological data underline the need for automated tools that could enhance accuracy and reduce workload in clinical practice. Advanced AI models, such as BiomedCLIP, offer potential solutions to these challenges by providing automated, high-sensitivity analysis tailored to medical data. Over the past year, there has been a global increase in interest in OSAIMs. This surge in attention is partly due to the release of advanced language models such as ChatGPT, produced by OpenAI. One of OpenAI's products is Contrastive Language-Image Pretraining (CLIP), which integrates both natural language and images. This model is capable of a wide range of tasks, including zero-shot image classification, image captioning, and visual question answering [6]. CLIP achieves its remarkable results by training on a diverse dataset of images and their textual descriptions, allowing the model to learn a shared embedding space, enabling it to process and understand both text and images more efficiently. Based on CLIP, several zero-shot models have already been developed, including medical models such as SDA-CLIP, a domain adaptation method for surgical tasks based on CLIP. It enables the recognition of surgical actions across different domains, such as virtual reality and real-life operations [7]. Another study utilizing this model was SleepCLIP, which demonstrated that CLIP could be effectively adapted to sleep staging tasks, showing potential for improving the diagnosis of sleep disorders [8]. BiomedCLIP, known in its full version as Biomed-CLIP-PubMedBERT_256-vit_base_patch16_224, is an advanced model that combines natural language processing with medical image analysis. It belongs to the category of vision-language foundation models, integrating both domains to perform complex tasks. This model was trained on a massive dataset, PMC-15M, containing 15 million \"image-description\" pairs from scientific articles available in PubMed Central. As a result, BiomedCLIP is capable of efficiently interpreting both medical text and images (Figure 1). BiomedCLIP stands out due to its architecture, which combines the capabilities of PubMedBERT, a model dedicated to text analysis, with a Vision Transformer optimized for working with biomedical data. This combination allows it to achieve outstanding results across many benchmark datasets, making it one of the top models in its category. The applications of BiomedCLIP are highly versatile. The model can be used for cross-modal retrieval (information search based on images or text), medical image classification, and answering questions related to those images.