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Background Bibliometrics refer to documents and citation-based measures that measure different aspects of performance of a journal, including impact, output and prestige. Objective The aim of this study was to collect bibliometric data of various Indian dermatology journals as well as Indian journals from other disciplines, in order to compare relative performances. Methods Journal metrics pertaining to various Indian journals, both from dermatology [Indian Journal of Dermatology, Venereology and Leprology (IJDVL), Indian Journal of Dermatology (IJD), Indian Dermatology Online Journal, Indian Journal of Pediatric Dermatology and International Journal of Trichology] and other disciplines [Indian Journal of Medical Research (IJMR), Indian Journal of Pediatrics (IJP), Indian Journal of Ophthalmology and Indian Journal of Pharmacology] were sought. Data pertaining to the following 8 metrics during the year 2021 was collected: Journal Impact factor, SCImago Journal Rank, h5-index, Eigenfactor score and normalized Eigenfactor Score, Journal Citation Indicator, Scimago Journal and Country Rank H-index, CiteScore and Source Normalized Impact per Paper. Results Among Indian dermatology journals, for the year 2021, IJDVL had the highest impact factor (2.217) and h-index (48). IJD led in terms of prestige metrics such as SCImago Journal Rank (0.403), Eigenfactor score (0.00231) and Source Normalized Impact per Paper (1.132). IJDVL underperformed with respect to an average dermatology journal on all three prestige metrics. Among selected journals from other disciplines, two (IJMR and IJP) had impact factor exceeding five, despite lagging behind IJDVL two years ago. Most had normalized scores exceeding 1, indicating better performance than an average journal from their respective fields. Limitations Non-inclusion of altmetrics related data Conclusion IJDVL is one of the leading Indian journals in the field of dermatology, followed closely by IJD. A rise in IJDVL influence is evident over the past decade, as evident by various metrics. However, the progress still trails behind the average of global dermatology journals as evident by the field-normalized journal metrics, indicating potential for further growth of journal influence. [ABSTRACT FROM AUTHOR]

This Review explores the transformative impact of artificial intelligence (AI) on the early detection of skin cancer, with a specific focus on melanoma, a potentially lethal form of the disease. Beginning with an overview of traditional diagnostic methods and their limitations, this paper delves into the evolution of AI within dermatology, emphasizing its application in image analysis and pattern recognition. A comprehensive examination of AI algorithms for melanoma detection, including machine learning and deep learning models, is provided. This Review critically assesses the performance metrics, training datasets, and comparative analyses with traditional methods. Addressing challenges such as data quality, interpretability, and ethical considerations, this paper outlines future directions, emphasizing ongoing research, algorithm improvements, and integration with clinical practices. Case studies and success stories highlight the real-world impact of AI in dermatology. This Review concludes by summarizing key findings and underlining the pivotal role of AI in revolutionizing early melanoma detection, with implications for personalized medicine and enhanced patient outcomes. [ABSTRACT FROM AUTHOR]

Treatment with Hedgehog Inhibitors in Gorlin‐Goltz syndrome (GGS) yields favourable objective clinical responses, yet secondary resistance and class‐related toxicity restrict treatment duration. This study aims to review current data on GGS patients undergoing vismodegib therapy, focusing on treatment duration, clinical outcomes and schedule modifications. A systematic search of the PubMed database was conducted for English articles from 1993 to 2023, identifying 31 papers suitable for inclusion. A total of 351 patients, with a mean age of 52 years, were analysed. The average treatment duration was 9.3 months for patients who discontinued treatment, and 25.1 months for those who continued vismodegib at the time this study was published. Vismodegib achieved a complete response rate of 44%. Treatment interruption predominantly occurred due to side effects (69.1%) and secondary resistance (9.1%). The use of alternative regimens, although not compromising efficacy, may enhance treatment compliance. Further investigations are warranted to ascertain the optimal treatment regimen and timeline for GGS patients. Schedule modifications offer promise in ameliorating side effects and facilitating long‐term treatment. [ABSTRACT FROM AUTHOR]

Hyaluronic acid (HA), as one of the main components of the extracellular matrix (ECM), plays an important role in the process of wound-healing and tissue-repair processes due to its unique properties and different physiological functions. HA has an ability to maintain a moist environment that promotes healing, the stimulation of growth factors and cellular constituents, and the migration of various cells essential for healing. This paper offers a review of HA use in the process of wound healing, with emphasis on hard-to-heal wounds, and examines its various applications in ophthalmology and otorhinolaryngology. It proves HA to be a versatile agent which finds its use in various fields of medicine for its antioxidant, anti-inflammatory, antibacterial properties and accelerated wound healing. [ABSTRACT FROM AUTHOR]

The field of dermatology is experiencing the rapid deployment of artificial intelligence (AI), from mobile applications (apps) for skin cancer detection to large language models like ChatGPT that can answer generalist or specialist questions about skin diagnoses. With these new applications, ethical concerns have emerged. In this scoping review, we aimed to identify the applications of AI to the field of dermatology and to understand their ethical implications. We used a multifaceted search approach, searching PubMed, MEDLINE, Cochrane Library and Google Scholar for primary literature, following the PRISMA Extension for Scoping Reviews guidance. Our advanced query included terms related to dermatology, AI and ethical considerations. Our search yielded 202 papers. After initial screening, 68 studies were included. Thirty-two were related to clinical image analysis and raised ethical concerns for misdiagnosis, data security, privacy violations and replacement of dermatologist jobs. Seventeen discussed limited skin of colour representation in datasets leading to potential misdiagnosis in the general population. Nine articles about teledermatology raised ethical concerns, including the exacerbation of health disparities, lack of standardized regulations, informed consent for AI use and privacy challenges. Seven addressed inaccuracies in the responses of large language models. Seven examined attitudes toward and trust in AI, with most patients requesting supplemental assessment by a physician to ensure reliability and accountability. Benefits of AI integration into clinical practice include increased patient access, improved clinical decision-making, efficiency and many others. However, safeguards must be put in place to ensure the ethical application of AI. [ABSTRACT FROM AUTHOR]

Many events, including the COVID‐19 pandemic, have accelerated the implementation of teledermatology pathways within dermatology departments and across healthcare organizations. Quality of Life (QoL) assessment in dermatology is also a rapidly developing field with a gradual shift from theory to practice. The purpose of this paper organized jointly by the European Academy of Dermatology and Venereology (EADV) Task Force (TF) on QoL and patient‐oriented outcomes and the EADV TF on teledermatology is to present current knowledge about QoL assessment during the use of teledermatology approaches, including data on health‐related (HR) QoL instruments used in teledermatology, comparison of influence of different treatment methods on HRQoL after face‐to‐face and teledermatology consultations and to make practical recommendations concerning the assessment of QoL in teledermatology. The EADV TFs made the following position statements: HRQoL assessment may be an important part in most of teledermatology activities; HRQoL assessment may be easily and effectively performed during teledermatology consultations. It is especially important to monitor HRQoL of patients with chronic skin diseases during lockdowns or in areas where it is difficult to reach a hospital for face‐to‐face consultation; regular assessment of HRQoL of patients with skin diseases during teledermatology consultations may help to monitor therapy efficacy and visualize individual patient's needs; we recommend the use of the DLQI in teledermatology, including the use of the DLQI app which is available in seven languages; it is important to develop apps for dermatology‐specific HRQoL instruments for use in children (for example the CDLQI and InToDermQoL) and for disease‐specific instruments. [ABSTRACT FROM AUTHOR]

Background: Coronavirus disease 2019 (COVID-19) has had a significant impact on dermatology, but to date no bibliometric analysis of this field has been identified. Therefore, the aim of this study was to perform a bibliometric indicator analysis of the worldwide scientific production of COVID-19 in dermatology. Materials and Methods: An advanced bibliographic search was performed in the Scopus database to identify articles on COVID-19 and dermatology from 2020 to 2021. The collected information was analysed with SciVal software. Bibliometric data were described through figures and summary tables. Results: A total of 1448 documents were collected and analysed. Torello Lotti was the author with the greatest scientific production; however, Esther Freeman had the greatest impact. Harvard University was the institution with the highest number of published articles. Most papers were published in the first quartiles. The United States and Italy were the leading countries in terms of production. Articles with international collaboration had the highest impact. Conclusion: Articles related to dermatology and COVID-19 are mostly published with American and Italian affiliations. In addition, there has been an increase in the distribution of articles published in the first quartile, which would reflect a growing interest in the community. Publications with international collaboration reported the highest impact, so future authors should take this into account. [ABSTRACT FROM AUTHOR]

Safflower (Carthamus tinctorius) oleosomes are unique organelles that house triglycerides and fatty acids and demonstrate a natural resilience to environmental stresses. There is recent growing interest in safflower oleosomes due to their potential applications in dermatology, especially as a carrier technology to improve drug penetration through the skin. This paper explores various aspects of safflower oleosomes, including their production, safety, absorption, and applications in photoprotection and epidermal remodeling. Oleosomes have shown encouraging results in targeted drug delivery in in vitro and in vivo animal models; however, human clinical research is required to determine their efficacy and safety in dermatology. Oleosomes are comprise a novel biotechnology that has the potential to transform sustainable and natural treatments in dermatology by utilizing their unique structure. Safflower oleosomes are stable lipid molecules that can deliver small and large molecules with high efficacy. This review will examine the current research findings and prospective future applications of oleosomes. [ABSTRACT FROM AUTHOR]

This article discusses the importance of appropriate statistical analysis and reporting in randomized clinical trial (RCT) reports. The British Journal of Dermatology (BJD) has implemented initiatives to enhance the quality of published papers, including more detailed statistical guidelines and a dedicated statistics section in the editorial team. The article presents an analysis of 55 RCT reports submitted to the BJD, focusing on the statistical concerns raised during the peer review process. The analysis reveals that some reports had no statistical comments, while others were rejected due to major statistical concerns. Issues included flaws in study design, analysis and reporting, and interpretations not supported by the study results. The majority of manuscripts were considered unsuitable for publication without revision, but authors were given the opportunity to make changes. The article emphasizes the importance of adhering to BJD guidelines for reporting RCT results to enhance clarity and understanding. [Extracted from the article]

[Display omitted] • A color normalization algorithm is proposed for digital pathology and dermatology. • A novel learning paradigm is presented to generalize heuristic algorithms. • The color normalization task is formulated as an image-to-image translation problem. • The generalization ability of the approach is tested on multiple external datasets. Color medical images introduce an additional confounding factor compared to conventional grayscale medical images: color variability. This variability can lead to inconsistent evaluation by clinicians and the misinterpretation or suboptimal learning process of automatic quantitative algorithms. To mitigate the potential negative consequences of color variability, several color normalization strategies have been developed, proving to be effective in standardizing image appearance. In this paper, we present a novel paradigm for color normalization using generative adversarial networks (GANs). Our method focuses on standardizing images in the field of digital pathology (stain normalization) and dermatology (color constancy), where high color variability is consistently observed. Specifically, we formulate the color normalization task as an image-to-image translation problem, ensuring a pixel-to-pixel correspondence between the original and normalized images. Our approach outperforms existing state-of-the-art methods in both the digital pathology and dermatology fields. Extensive validation using public datasets demonstrate the effectiveness of our color normalization results on entirely external test sets. Our framework exhibits strong generalization capability on unseen data, making it suitable for inclusion in the pipeline of automatic quantitative algorithms to reduce color variability and improve segmentation and/or classification performance. Lastly, we provide the source code of our models to encourage open science. [ABSTRACT FROM AUTHOR]