Although artificial intelligence is rapidly advancing, its application in translational dermatological research remains limited. In this study, we established an artificial intelligence–based image analysis workflow for the quantification of proliferation and apoptosis within the human epidermis using 3,3′-diaminobenzidine–based immunohistochemical staining. Human skin cultured in a 3-dimensional in vivo model was processed using paraffin embedding and immunohistochemical staining for Ki-67 and cleaved Caspase-3. We implemented an artificial intelligence–based 2-model workflow: a custom-trained convolutional neural network–based model for cell detection and a semantic-segmentation model that specifically segregates the epidermis. By combining both models, we restricted cell annotation and classification to the epidermis—our structure of interest—enabling epidermis-constrained quantification on whole-slide images. Validation against manual counts showed high agreement for both markers (Ki-67 mean accuracy = 95.43%; cleaved Caspase-3 = 97.0%) across staining batches and time points. To test the experimental applicability of our workflow, we treated human skin cultured on the chorioallantoic membrane with hydroxytyrosol. The artificial intelligence–based workflow minimized subjective bias and provided a coherent readout, showing reduced proliferation without inducing apoptotic responses in hydroxytyrosol-treated samples. Overall, the workflow achieved strong performance with modest training effort and annotation, offering a scalable approach that supports translational dermatological research and holds potential for dermatopathological applications.