## 4 Discussion  
In our study, we analyzed the responses of four AI models--Gemini, Gemini Advanced, Microsoft Copilot, and ChatGPT 4--regarding the analysis of WB imagery, affirming the posited hypothesis. Each AI model demonstrated the ability to conduct fundamental basic analysis and interpretation of WB images, identifying key elements such as the presence and characteristics of individual protein bands and describing the fundamental steps of the WB procedure. Gemini distinguished itself with a detailed description of the WB process and band interpretation, emphasizing their biological significance. Gemini Advanced focused on identifying and interpreting specific bands, particularly highlighting Ub-48 UBB+1 dimers. Microsoft Copilot provided a general overview of the WB image, noting key sections and bands, but with less technical depth. ChatGPT 4, on the other hand, offered a detailed interpretation of bands in the context of patient samples and standards, encompassing both biological and technical aspects. The differences in detail and context among the models were consistent with the assumptions of the hypothesis, allowing it to be considered confirmed. Our findings indicate significant differences in the manner of WB data interpretation by different AI models, which significantly impacts their utility in biomedical research. Analyzing the responses of the four AI models, notable differences were observed in the depth of details, specificity of band interpretation, and the context and application of the provided information. A fundamental observation is that all models effectively recognize and describe WB analysis, focusing on identifying specific protein bands. This basic competency in recognizing key elements of the WB process aligns with the general understanding of molecular biology techniques trained in AI models. However, differences in the level of accuracy and depth of interpretation among the models underscore that not all AI tools are equally effective in delivering the detailed and contextual information necessary for advanced scientific analysis. The Gemini model stands out with a detailed description of the WB process and precise band interpretation, which can be useful in detailed biological and biochemical studies. This model appears to be more useful in contexts where a deep technical analysis and understanding of molecular processes are required. In contrast, Gemini Advanced, focusing on the presence of Ub-48 UBB+1 dimers, demonstrates the ability to identify specific elements in samples, which can be useful in more targeted studies, such as research on specific protein modifications. Microsoft Copilot, providing a general overview of the WB process, can be helpful in educational situations or preliminary analyses, where detailed technical knowledge is not crucial but a general understanding of the process is still needed. ChatGPT 4, presenting detailed interpretations of bands in the context of patient samples and control standards, is particularly useful in clinical and diagnostic research, where understanding the relationship between bands and specific biological states is key. These differences can be attributed to the specific training and architecture of each model. Each model has been trained and optimized in different contexts, influencing its ability to process and interpret specific scientific data. For example, models trained on a broad range of scientific literature may perform better with general interpretations, while specialized models may excel in identifying and analyzing detailed technical aspects. In summary, our results indicate that while each AI model can be useful in WB data analysis, their effectiveness and utility depend on the research context. The choice of AI tool should be dictated by the specific needs of the study, domain knowledge, and requirements for technical and contextual depth of analysis. In the realm of proteomics, the integration of AI has shown significant advancements. For instance, [PERSON] et al.