Background: Breast cancer is a heterogeneous disease with distinct molecular subtypes, each requiring tailored therapeutic strategies. Accurate classification of these subtypes is crucial for optimizing treatment and improving patient outcomes. While immunohistochemistry remains the gold standard for subtyping, it is invasive and may not fully capture tumor heterogeneity. Artificial Intelligence (AI), particularly Deep Learning (DL), offers a promising non-invasive alternative by analyzing medical imaging data. Methods: In this study, we propose a multimodal DL model that integrates mammography images with clinical metadata to classify breast lesions into five categories: benign, luminal A, luminal B, HER2-enriched, and triple-negative. Using the publicly available Chinese Mammography Database (CMMD), our model was trained and evaluated on a dataset of 4056 images from 1775 patients. Results: The proposed multimodal approach significantly outperformed a unimodal model based solely on mammography images, achieving an AUC of 88.87% for multiclass classification of these five categories, compared to 61.3% AUC for the unimodal model. Conclusions: These findings highlight the potential of multimodal AI-driven approaches for non-invasive breast cancer subtype classification, paving the way for improved diagnostic precision and personalized treatment strategies.

Objective: Peripheral neuropathies contribute to patient disability but may be diagnosed late or missed altogether due to late referral, limitation of current diagnostic methods and lack of specialized testing facilities. To address this clinical gap, we developed NeuropathAI, an interpretable deep learning–based multiclass classification system for rapid, automated diagnosis and differentiation of 88 patients with diabetic peripheral neuropathy (DPN), chemotherapy‐induced peripheral neuropathy (CIPN), chronic inflammatory demyelinating polyneuropathy (CIDP), and human immunodeficiency virus‐associated sensory neuropathy (HIV‐SN). Methods: A deep learning–based multiclass system was developed to analyze corneal nerve images. These images were preprocessed to train and validate the proposed model and the diagnostic utility was evaluated from the accuracy, F1 ‐score and area under the curve to derive sensitivity, specificity and precision. Results: NeuropathAI achieved excellent results: AUC—96.75%, sensitivity—83.87%, specificity—95.07%, and demonstrated excellent discrimination for CIDP, CIPN, HIV‐SN and DPN with one‐vs‐all AUC scores of 97%, 93.1%, 99.7% and 96.9%, respectively. Explainability visualization through heatmaps demonstrated that regions of decision making by the model localized to areas with nerve fiber loss, enhancing interpretability. Interpretation: NeuropathAI achieved rapid and accurate diagnosis of four of the most prevalent peripheral neuropathies globally, underscoring the potential of artificial intelligence–driven corneal image analysis for the rapid diagnosis and differentiation of peripheral neuropathies.