This literature review introduces the integration of Large Language Models (LLMs) in the field of dermatopathology, outlining their potential benefits, challenges, and prospects. It discusses the changing landscape of dermatopathology with the emergence of LLMs. The potential advantages of LLMs include a streamlined generation of pathology reports, the ability to learn and provide up-to-date information, and simplified patient education. Existing instances of LLMs encompass diagnostic support, research acceleration, and trainee education. Challenges involve biases, data privacy and quality, and establishing a balance between AI and dermatopathological expertise. Prospects include the integration of LLMs with other AI technologies to improve diagnostics and the improvement of multimodal LLMs that can handle both text and image input. Our implementation guidelines highlight the importance of model transparency and interpretability, data quality, and continuous oversight. The transformative potential of LLMs in dermatopathology is underscored, with an emphasis on a dynamic collaboration between artificial intelligence (AI) experts (technical specialists) and dermatopathologists (clinicians) for improved patient outcomes.

Background: Fluoroquinolones, available in topical and oral formulations, are used to manage bacterial skin and soft tissue infections, including Pseudomonas aeruginosa, atypical mycobacteria, and select multidrug-resistant Gram-negative organisms. Their excellent tissue penetration, bactericidal activity, and convenient dosing make them effective for certain skin and soft tissue infections. However, their use is limited by potential safety concerns, including tendinopathy (odds ratio up to 9.1 in corticosteroid users), QT interval prolongation with risk of torsades de pointes, phototoxicity, and rising antimicrobial resistance. Methods: A literature search of PubMed, Scopus, and Web of Science was conducted for articles from January 1985 to April 2025 with the search terms (quinolone OR fluoroquinolone) AND (dermatology OR “skin and soft tissue infection” OR “skin structure infection”). Abstracts and presentations were excluded. A Google search used the same terms for articles from government regulatory agencies. Results: This review provides practical guidance on the clinical use of topical and oral fluoroquinolones in dermatology. Delafloxacin demonstrated over 90% cure rates in trials for complicated skin infections. However, serious safety concerns remain, including a ninefold increase in tendinopathy risk among older adults on corticosteroids and corrected QT intervals exceeding 500 milliseconds in high-risk patients. Phototoxicity varies, with agents like sparfloxacin linked to heightened ultraviolet sensitivity. Resistance to ciprofloxacin exceeds 20 percent in Escherichia coli and P. aeruginosa in some populations. Culture-based prescribing, shorter treatment courses, and preference for topical treatments can reduce risk and preserve efficacy. Conclusions: Fluoroquinolones remain clinically useful in dermatology when prescribed selectively. Their appropriate use requires careful attention to patient risk factors along with their evolving resistance patterns and ongoing stewardship efforts.

Melanoma subtyping plays a vital role in histopathological diagnosis, informing prognosis and, in some cases, guiding targeted therapy. However, conventional histologic classification is constrained by inter-rater reliability, morphologic overlap, and the underrepresentation of rare subtypes. Deep learning (DL)—particularly convolutional neural networks (CNNs)—presents a compelling opportunity to enhance diagnostic precision and reproducibility through automated analysis of histopathologic slides. This review examines the clinical importance and diagnostic challenges of melanoma subtyping, outlines core DL methodologies in dermatopathology, and synthesizes current advances in applying DL to subtype classification. Pertinent limitations including dataset imbalance, a lack of interpretability, and domain generalizability are discussed. Additionally, emerging directions such as multimodal integration, synthetic data generation, federated learning, and explainable AI are highlighted as potential solutions. As these technologies mature, DL holds considerable promise in advancing melanoma diagnostics and supporting more personalized, accurate, and equitable patient care.