Dermato, Volume 4, Issue 4 (December 2024) – 3 articles

With the growing complexity of skin disorders and the challenges of traditional diagnostic methods, AI offers exciting new solutions that can enhance the accuracy and efficiency of dermatological assessments. Reflectance Confocal Microscopy (RCM) stands out as a non-invasive imaging technique that delivers detailed views of the skin at the cellular level, proving its immense value in dermatology. The manual analysis of RCM images, however, tends to be slow and inconsistent. By combining artificial intelligence (AI) with RCM, this approach introduces a transformative shift toward precise, data-driven dermatopathology, supporting more accurate patient stratification, tailored treatments, and enhanced dermatological care. Advancements in AI are set to revolutionize this process. This paper explores how AI, particularly Convolutional Neural Networks (CNNs), can enhance RCM image analysis, emphasizing machine learning (ML) and deep learning (DL) methods that improve diagnostic accuracy and efficiency. The discussion highlights AI’s role in identifying and classifying skin conditions, offering benefits such as a greater consistency and a reduced strain on healthcare professionals. Furthermore, the paper explores AI integration into dermatological practices, addressing current challenges and future possibilities. The synergy between AI and RCM holds the potential to significantly advance skin disease diagnosis, ultimately leading to better therapeutic personalization and comprehensive dermatological care. Full article

Background: Lichen sclerosus (LS) is a chronic, inflammatory dermatosis that affects both genital and extragenital sites. It is often difficult to treat and may lead to a variety of complications if not adequately treated. The mainstay of therapy involves topical corticosteroids, topical calcineurin inhibitors, and systemic immunomodulators. Although a variety of topical, oral, and procedural therapies are available, a review comparing relative efficacy is lacking. To this end, this systematic review aimed to summarize the literature regarding treatment modalities and their respective response rates in patients with genital LS. Methods: A literature search was conducted in accordance with PRISMA guidelines. Results: This review qualitatively summarizes information from 31 randomized controlled trials, encapsulating a total of 1507 patients with LS, the majority of which were female (n = 1374, 91%). Topical corticosteroids, the mainstay of therapy for LS, were discussed throughout the literature, and proved to be more efficient than topical calcineurin inhibitors, topical hormonal therapy, topical vitamin E oil and cold cream. However, other treatment modalities proved to be more efficient than topical corticosteroids, including CO₂ and Nd:YAG laser therapies, and the addition of polydeoxyribonucleotide intradermal injections, to steroid therapy. Finally, other modalities that proved to be efficient in the treatment of LS included silk undergarments, human fibroblast lysate cream, platelet-rich plasma, acitretin, and surgical intervention. The risk of bias was assessed using the Cochrane risk-of-bias tool for randomized trials. Limitations included the inclusion of only randomized controlled trials, moderate or high risk of bias, and heterogeneity in treatment regimens, among others. Conclusion: Although high-potency topical corticosteroids have validated efficacy in the management of LS, other treatment modalities, including steroid-sparing agents and/or procedural adjuncts, have been demonstrated to have a beneficial role in the treatment of LS. Full article

Large language models (LLMs) are trained using large datasets and may be applied to language-based tasks. Studies have demonstrated their ability to perform and pass postgraduate medical examinations, and with the increasingly sophisticated deep learning algorithms and incorporation of image-analysis capabilities, they may also be applied to the Specialty Certificate Examination (SCE) in Dermatology. The Dermatology SCE sample questions were used to assess the performance of five freely available and high-performance LLMs. The LLMs’ performances were recorded by comparing their output on multiple-choice questions against the sample answers. One hundred questions, four of which included photographs, were entered into the LLMs. The responses were recorded and analysed, with the pass mark set at 77%. The accuracies for Claude-3.5 Sonnet, Copilot, Gemini, ChatGPT-4o, and Perplexity were 87, 88, 75, 90, and 87, respectively (p = 0.023). The LLMs were generally capable of interpreting and providing reasoned responses to clinical scenarios and clinical data. This continues to demonstrate the potential of LLMs in both medical education and clinical settings. Full article