Surgical pathology remains the cornerstone for the diagnosis, staging and prognostic stratification of cutaneous melanoma. The emergence of digital pathology and artificial intelligence (AI), notably deep learning, is transforming this field. Convolutional neural networks trained on large repositories of digitized histological slides demonstrate diagnostic capabilities comparable, and in specific tasks superior, to board-certified dermatopathologists. Current applications encompass the automated classification of melanocytic lesions, including the challenging differentiation between benign melanocytic naevi and early melanoma. Beyond morphological diagnosis, AI algorithms extract virtual biomarkers directly from haematoxylin-eosin-stained slides, predicting underlying molecular alterations, including BRAF mutational status, and offering a transformative approach to prognostic stratification, enabling the extraction of morphological and spatial features that are imperceptible to the human eye. Challenges remain, including dataset bias, inter-laboratory standardisation of whole slide imaging workflows, model interpretability, and medico-legal accountability. The regulatory landscape is rapidly evolving: the European Union Artificial Intelligence Act classifies medical AI as high-risk. The 2024 FDA de novo authorization of DermoSensor, an adjunctive AI-enabled device for assessing suspicious skin lesions, illustrates broader progress in dermatologic AI, although it does not concern surgical pathology. This review provides a comprehensive overview of the current state and future perspectives of AI in management of cutaneous melanoma. These new tools will augment the central role of the pathologist in precision oncology, in refining adjuvant treatment decisions and surveillance strategies in routine clinical practice.
Artificial intelligence (AI) comprises computational methods capable of performing tasks associated with human cognition, and includes specialized subfields such as machine learning, deep learning, convolutional neural networks for image analysis, and large language models for text-based workflows. In dermatology, these methods are increasingly used across research and clinical practice, supporting drug discovery, diagnosis, decision-making, and personalized treatment. In drug discovery, AI can accelerate target identification, support in silico compound design, and predict the effectiveness and safety profiles of compounds. It also offers opportunities for drug repurposing, helping identify candidates for conditions with limited treatment options. In clinical practice, AI enhances teledermatology and imaging-based assessment, providing consistent lesion analysis, severity evaluation, and triage support across malignant, inflammatory, and infectious skin diseases. Beyond diagnosis, AI is beginning to play a significant role in supporting therapeutic decision-making. Clinical decision-support systems and multimodal AI tools assist clinicians by organizing information, suggesting management strategies, monitoring disease progression, and helping align treatment choices with current guidelines. AI models can further estimate treatment response, durability, and the likelihood of therapy adjustment, creating a basis for more individualized care. Despite these advances, significant challenges remain. Bias in training data, limited real-world validation, unclear regulatory frameworks, concerns over interpretability, and the need for patient and clinician trust remain ongoing barriers. This review aims to provide an up-to-date overview of the emerging application domains of AI in dermatology.
Artificial intelligence (AI) is rapidly transforming medical research and scholarly publishing, reshaping how scientific knowledge is produced, evaluated, and disseminated. Initially developed as a decision-support tool, AI has evolved into a complex ecosystem encompassing machine learning, deep learning, and large language models, with applications spanning data analysis, diagnostic support, evidence synthesis, manuscript preparation, peer review, and post-publication analytics. These technologies offer substantial benefits, including accelerated research workflows, improved analytical precision, enhanced reproducibility, and expanded access to scientific communication, particularly for early-career investigators and non-native English authors. However, the integration of generative AI introduces significant challenges. Persistent risks include algorithmic bias, hallucinated or misattributed citations, erosion of authorship accountability, confidentiality concerns, and the potential degradation of peer review integrity. As AI-generated outputs increasingly resemble human scholarly work, longstanding norms surrounding authorship, transparency, and responsibility are being reexamined. In response, editorial organizations, journals, and global health authorities have begun to establish governance frameworks emphasizing disclosure, human verification, and ethical boundaries for AI use. This narrative review synthesizes current evidence on the evolution and applications of AI in medical research and publishing, critically examines associated risks and ethical dilemmas, and reviews emerging regulatory and editorial guidance. Finally, it outlines future directions centered on explainable and auditable AI, standardized AI literacy, and hybrid human-AI workflows. Ensuring that AI remains a tool for augmentation rather than replacement will be essential to preserving trust, rigor, and integrity in medical scholarship as these technologies become increasingly embedded in the scientific enterprise.
Artificial intelligence (AI) has moved from proof-of-concept studies in dermatology to selective, real-world clinical use, particularly in image-based triage, lesion assessment, and workflow augmentation. Dermatology is uniquely suited to AI because much of diagnostic reasoning depends on visual information (clinical photos, dermoscopy, reflectance confocal microscopy, optical coherence tomography) plus context (history, distribution, symptoms, treatments, and risk factors). Yet translation into routine care depends less on eye-catching benchmark accuracy and more on careful validation, robust reference standards, fairness across skin tones and devices, usability within clinical workflows, and continuous post-deployment monitoring for performance and safety. This review summarizes leading diagnostic applications of AI in dermatology, provides a practical framework for clinical validation, and highlights real-world deployment patterns including teledermatology triage, melanoma risk workflows, inflammatory dermatoses severity scoring, and AI-assisted dermatopathology. We emphasize that "AI performance" is not a single number: the intended use (screening vs. diagnosis vs. referral prioritization), operating thresholds, prevalence, human-AI interaction, and downstream clinical actions determine real impact. Finally, we outline implementation considerations (governance, privacy, regulatory expectations, and monitoring) and propose practical steps for clinics and health systems seeking safe adoption.
