Background: Cutaneous melanoma (CM) poses significant diagnostic challenges due to its biological heterogeneity and the subjective interpretation of histopathologic criteria. While early and accurate diagnosis remains critical for patient outcomes, conventional pathology is limited by interobserver variability and diagnostic ambiguity, especially in borderline lesions. Objective: This narrative review explores the integration of digital pathology (DP) and artificial intelligence (AI)—including deep learning (DL), machine learning (ML), and interpretable models—into the histopathologic workflow for CM diagnosis. Methods: We systematically searched PubMed, Scopus, and Web of Science (2013–2025) for studies using whole slide imaging (WSI) and AI to assist melanoma diagnosis. We categorized findings across five domains: WSI-based classification models, feature extraction (e.g., mitoses, ulceration), spatial modeling and TIL analysis, molecular prediction (e.g., BRAF mutation), and interpretable pipelines based on nuclei morphology. Results: We included 87 studies with diverse AI methodologies. Convolutional neural networks (CNNs) achieved diagnostic accuracy comparable to expert dermatopathologists. U-Net and Mask R-CNN models enabled robust detection of critical histologic features, while nuclei-level analyses offered explainable classification strategies. Spatial and morphometric modeling allowed quantification of tumor–immune interactions, and select models inferred molecular alterations directly from H&E slides. However, generalizability remains limited due to small, homogeneous datasets and lack of external validation. Conclusions: AI-enhanced digital pathology holds transformative potential in CM diagnosis, offering accuracy, reproducibility, and interpretability. Yet, clinical integration requires multicentric validation, standardized protocols, and attention to workflow, ethical, and medico-legal challenges. Future developments, including multimodal AI and integration into molecular tumor boards, may redefine diagnostic precision in melanoma.

Skin aging is a multidimensional biological process driven by intrinsic chronological changes, exposomal stress, endocrine–metabolic shifts, extracellular matrix remodeling, inflammaging, oxidative injury, barrier impairment, and microbiome dysbiosis. This review integrates current evidence on the endocrine–microbiome–skin axis and evaluates microbiome-directed bioactive strategies for preserving cutaneous homeostasis during aging. Particular attention is given to probiotics, prebiotics, postbiotics, synbiotics, phytoestrogens, polyphenols, bioactive peptides, antioxidants, mitochondrial protectors, adaptogens, and metabolic modulators. Their mechanisms are discussed in relation to collagen homeostasis, mitochondrial function, lipid barrier integrity, immune regulation, microbial metabolite signaling, and systemic endocrine–metabolic status. The review also examines advanced delivery platforms, including liposomes, solid lipid nanoparticles, nanostructured lipid carriers, polymeric nanocarriers, nanoemulsions, encapsulated microbiome-active systems, and stimuli-responsive carriers, emphasizing their potential to improve compound stability, skin retention, controlled release, and target-site precision. Translational limitations are critically addressed, including strain and formulation specificity, insufficient long-term safety data, incomplete nanocarrier toxicology, regulatory ambiguity, and the need for personalization according to hormonal, metabolic, and microbiome profiles. Overall, microbiome-directed bioactive compounds combined with precision delivery systems represent a promising, but still evolving, strategy for delaying skin aging and restoring cutaneous homeostasis.