Simple Summary Breast cancer is a complex disease that can be classified into different biological subtypes. Correctly identifying these subtypes is essential in determining the most effective treatment for each patient. However, current methods such as gene expression testing and immunohistochemistry are either expensive, time-consuming, or not widely available in all healthcare settings. In this study, we explored whether a computer-based approach using artificial intelligence can accurately predict breast cancer subtypes by analyzing routine pathology slides stained with hematoxylin and eosin. This real-world validation study shows that this method can identify certain subtypes with promising accuracy, offering a faster and more accessible alternative to existing techniques. This research may help improve diagnostic processes, especially in hospitals with limited resources, and support more personalized treatment decisions for patients with breast cancer.Abstract Background/Objectives: Breast cancer (BC) is the most commonly diagnosed cancer in women and the leading cause of cancer-related deaths globally. Molecular subtyping is crucial for prognosis and treatment planning, with immunohistochemistry (IHC) being the most commonly used method. However, IHC has limitations, including observer variability, a lack of standardization, and a lack of reproducibility. Gene expression profiling is considered the ground truth for molecular subtyping; unfortunately, this is expensive and inaccessible to many institutions. This study investigates the potential of an artificial intelligence (AI) model to predict BC molecular subtypes directly from hematoxylin and eosin (H&E)-stained whole-slide images (WSIs). Methods: A pretrained deep learning framework based on multiple-instance learning (MIL) was validated on the Stavanger Breast Cancer (SBC) dataset, consisting of 538 BC cases. Three classification tasks were assessed, including two-class [triple negative BC (TNBC) vs. non-TNBC], three-class (luminal vs. HER2-positive vs. TNBC), and four-class (luminal A vs. luminal B vs. HER2-positive vs. TNBC) groups. Performance metrics were used for the evaluation of the AI model. Results: The AI model demonstrated strong performance in distinguishing TNBC from non-TNBC (AUC = 0.823, accuracy = 0.833, F1-score = 0.824). However, performance declined with an increasing number of classes. Conclusions: The study highlights the potential of AI in BC molecular subtyping from H&E WSIs, offering an easily applicable and standardized method to IHC. Future improvements should focus on optimizing multi-class classification and validating AI-based methods against gene expression analyses for enhanced clinical applicability.