Hasil untuk "Dermatology"

A. Finlay, G. K. Khan

T. Fitzpatrick

Introduction biology and pathophysiology of skin disorders presenting in the skin and mucous membranes dermatology and internal medicine diseases due to microbial agents therapeutics paediatric and geriatric dermatology.

A. Rook, T. Burns

R. Daneshjou, Kailas Vodrahalli, Weixin Liang et al.

An estimated 3 billion people lack access to dermatological care globally. Artificial intelligence (AI) may aid in triaging skin diseases and identifying malignancies. However, most AI models have not been assessed on images of diverse skin tones or uncommon diseases. Thus, we created the Diverse Dermatology Images (DDI) dataset—the first publicly available, expertly curated, and pathologically confirmed image dataset with diverse skin tones. We show that state-of-the-art dermatology AI models exhibit substantial limitations on the DDI dataset, particularly on dark skin tones and uncommon diseases. We find that dermatologists, who often label AI datasets, also perform worse on images of dark skin tones and uncommon diseases. Fine-tuning AI models on the DDI images closes the performance gap between light and dark skin tones. These findings identify important weaknesses and biases in dermatology AI that should be addressed for reliable application to diverse patients and diseases.

A. Adamson, Avery Smith

Bailey Brame, Peter F. Canning

elsevier small animal dermatology 4th edition , small animal dermatology a color atlas and therapeutic, muller and kirk s small animal dermatology 7th edition, amazon com small animal dermatology, small animal dermatology a color atlas and , small animal dermatology a color atlas and therapeutic, small animal dermatology 3rd edition pdf 2018 blackwell, small animal dermatology sciencedirect, small animal dermatology a color atlas and therapeutic, small animal dermatology a color atlas and therapeutic, derm pgc and gpcert small animal dermatology improve, small animal dermatology advanced cases self , muller and kirk s small animal dermatology 6th edition, small animal dermatology a color atlas and therapeutic, small animal dermatology secrets vetbooks, small animal dermatology a color atlas and , saunders solutions in veterinary practice small animal, small animal dermatology a color atlas and therapeutic, small animal dermatology by jane coatesworth overdrive, small animal dermatology a color atlas and therapeutic, small animal dermatology , small animal dermatology 9780323376518 us elsevier, small animal dermatology a color atlas and therapeutic, small animal dermatology texas a amp m veterinary , small animal dermatology q amp a 16

D. X.Du-Harpuri, D. F.M.Watti, D. N.M.Luscombei et al.

In the past, the skills required to make an accurate dermatological diagnosis have required exposure to thousands of patients over many years. However, in recent years, artificial intelligence (AI) has made enormous advances, particularly in the area of image classification. This has led computer scientists to apply these techniques to develop algorithms that are able to recognize skin lesions, particularly melanoma. Since 2017, there have been numerous studies assessing the accuracy of algorithms, with some reporting that the accuracy matches or surpasses that of a dermatologist. While the principles underlying these methods are relatively straightforward, it can be challenging for the practising dermatologist to make sense of a plethora of unfamiliar terms in this domain. Here we explain the concepts of AI, machine learning, neural networks and deep learning, and explore the principles of how these tasks are accomplished. We critically evaluate the studies that have assessed the efficacy of these methods and discuss limitations and potential ethical issues. The burden of skin cancer is growing within the Western world, with major implications for both population skin health and the provision of dermatology services. AI has the potential to assist in the diagnosis of skin lesions and may have particular value at the interface between primary and secondary care. The emerging technology represents an exciting opportunity for dermatologists, who are the individuals best informed to explore the utility of this powerful novel diagnostic tool, and facilitate its safe and ethical implementation within healthcare systems.

Christeen Samuel, Hannah L Cornman, Anusha Kambala et al.

Janus kinase (JAK) inhibitors are disease-modifying agents with efficacy in treating a spectrum of burdensome dermatologic conditions. The US Food and Drug Administration (FDA) recently placed a black box warning on this class of medications due to safety concerns based on data from studies investigating tofacitinib in patients with rheumatoid arthritis. Here we provide an overview of the timeline of FDA approval of JAK inhibitors in dermatology. We also discuss the available safety profiles of approved oral JAK1 inhibitors, namely abrocitinib and upadacitinib, oral baricitinib, a JAK1/2 inhibitor, deucravacitinib, a Tyk2 inhibitor, and the topical JAK1/2 inhibitor ruxolitinib in dermatology patients. Additionally, we offer suggestions for initial screening and laboratory monitoring for patients receiving JAK inhibitors. We found that the rates of venous thromboembolism reported in trials ranged from no events to 0.1–0.5% in dermatology-specific phase 3 clinical trials compared with no events in the placebo. The rates of cardiovascular events ranged from no events to 0.4–1.2% compared with no events to 0.5–1.2% in the placebo. The rates of serious infections were 0.4–4.8% compared with no events to 0.5–1.3% in the placebo. The rates of nonmelanoma skin cancer (NMSC) ranged from no event to 0.6–0.9% compared with no events in the placebo. The rates of non-NMSC ranged from no event to 0.2–0.7% compared with no event to 0.6% in the placebo. Most patients who developed these adverse events had risk factors for the specific event. The most common adverse events of oral JAK inhibitors included upper respiratory infections, nasopharyngitis, nausea, headache, and acne. Dermatologists should consider patients’ baseline risk factors for developing serious complications when prescribing oral JAK inhibitors.

Zhouxiao Li, K. Koban, T. Schenck et al.

Background: Thanks to the rapid development of computer-based systems and deep-learning-based algorithms, artificial intelligence (AI) has long been integrated into the healthcare field. AI is also particularly helpful in image recognition, surgical assistance and basic research. Due to the unique nature of dermatology, AI-aided dermatological diagnosis based on image recognition has become a modern focus and future trend. Key scientific concepts of review: The use of 3D imaging systems allows clinicians to screen and label skin pigmented lesions and distributed disorders, which can provide an objective assessment and image documentation of lesion sites. Dermatoscopes combined with intelligent software help the dermatologist to easily correlate each close-up image with the corresponding marked lesion in the 3D body map. In addition, AI in the field of prosthetics can assist in the rehabilitation of patients and help to restore limb function after amputation in patients with skin tumors. The aim of the study: For the benefit of patients, dermatologists have an obligation to explore the opportunities, risks and limitations of AI applications. This study focuses on the application of emerging AI in dermatology to aid clinical diagnosis and treatment, analyzes the current state of the field and summarizes its future trends and prospects so as to help dermatologists realize the impact of new technological innovations on traditional practices so that they can embrace and use AI-based medical approaches more quickly.

F. Solimani, K. Meier, K. Ghoreschi

Accumulating data on cellular and molecular pathways help to develop novel therapeutic strategies in skin inflammation and autoimmunity. Examples are psoriasis and atopic dermatitis, two clinically and immunologically well-defined disorders. Here, the elucidation of key pathogenic factors such as IL-17A/IL-23 on the one hand and IL-4/IL-13 on the other hand profoundly changed our therapeutic practice. The knowledge on intracellular pathways and governing factors is shifting our attention to new druggable molecules. Multiple cytokine receptors signal through Janus kinases (JAKs) and associated signal transducer and activators of transcription (STATs). Inhibition of JAKs can simultaneously block the function of multiple cytokines. Therefore, JAK inhibitors (JAKi) are emerging as a new class of drugs, which in dermatology can either be used systemically as oral drugs or locally in topical formulations. Inhibition of JAKs has been shown to be effective in various skin disorders. The first oral JAKi have been recently approved for the treatment of rheumatoid arthritis and psoriatic arthritis. Currently, multiple inhibitors of the JAK/STAT pathway are being investigated for skin diseases like alopecia areata, atopic dermatitis, dermatomyositis, graft-versus-host-disease, hidradenitis suppurativa, lichen planus, lupus erythematosus, psoriasis, and vitiligo. Here, we aim to discuss the immunological basis and current stage of development of JAKi in dermatology.

Andrzej Grzybowski, Kai Jin, Hongkang Wu

Artificial intelligence (AI) in medicine and dermatology brings additional challenges related to bias, transparency, ethics, security, and inequality. Bias in AI algorithms can arise from biased training data or decision-making processes, leading to disparities in healthcare outcomes. Addressing bias requires carefully examining the data used to train AI models and implementing strategies to mitigate bias during algorithm development. Transparency is another critical challenge, as AI systems often operate as black boxes, making it difficult to understand how decisions are reached. Ensuring transparency in AI algorithms is vital to gaining trust from both patients and healthcare providers. Ethical considerations arise when using AI in healthcare, including issues such as informed consent, privacy, and the responsibility for the decisions made by AI systems. It is essential to establish clear guidelines and frameworks that govern the ethical use of AI, including maintaining patient autonomy and protecting sensitive health information. Security is a significant concern in AI systems, as they rely on vast amounts of sensitive patient data. Protecting these data from unauthorized access, breaches, or malicious attacks is paramount to maintaining patient privacy and trust in AI technologies. Lastly, the potential for inequality arises if AI technologies are not accessible to all populations, leading to a digital divide in healthcare. Efforts should be made to ensure that AI solutions are affordable, accessible, and tailored to the needs of diverse communities, mitigating the risk of exacerbating existing healthcare disparities. Addressing these challenges is crucial for AI's responsible and equitable integration in medicine and dermatology.

Stephanie Chan, V. Reddy, B. Myers et al.

Machine learning (ML) has the potential to improve the dermatologist’s practice from diagnosis to personalized treatment. Recent advancements in access to large datasets (e.g., electronic medical records, image databases, omics), faster computing, and cheaper data storage have encouraged the development of ML algorithms with human-like intelligence in dermatology. This article is an overview of the basics of ML, current applications of ML, and potential limitations and considerations for further development of ML. We have identified five current areas of applications for ML in dermatology: (1) disease classification using clinical images; (2) disease classification using dermatopathology images; (3) assessment of skin diseases using mobile applications and personal monitoring devices; (4) facilitating large-scale epidemiology research; and (5) precision medicine. The purpose of this review is to provide a guide for dermatologists to help demystify the fundamentals of ML and its wide range of applications in order to better evaluate its potential opportunities and challenges.

Miguel Aristizabal, Katherine V Jiménez-Orrego, M. D. Caicedo‐León et al.

Microplastics (MPs) and nanoplastics (NPs) have become a growing concern in dermatology due to their widespread presence in cosmetic formulations and the environment. These minuscule synthetic polymer particles prompt an essential exploration of their potential impact on dermatological homeostasis.

Abdurrahim Yilmaz, Ozan Erdem, Ece Gokyayla et al.

Vision-language models (VLMs) are increasingly important in medical applications; however, their evaluation in dermatology remains limited by datasets that focus primarily on image-level classification tasks such as lesion recognition. While valuable for recognition, such datasets cannot assess the full visual understanding, language grounding, and clinical reasoning capabilities of multimodal models. Visual question answering (VQA) benchmarks are required to evaluate how models interpret dermatological images, reason over fine-grained morphology, and generate clinically meaningful descriptions. We introduce DermaBench, a clinician-annotated dermatology VQA benchmark built on the Diverse Dermatology Images (DDI) dataset. DermaBench comprises 656 clinical images from 570 unique patients spanning Fitzpatrick skin types I-VI. Using a hierarchical annotation schema with 22 main questions (single-choice, multi-choice, and open-ended), expert dermatologists annotated each image for diagnosis, anatomic site, lesion morphology, distribution, surface features, color, and image quality, together with open-ended narrative descriptions and summaries, yielding approximately 14.474 VQA-style annotations. DermaBench is released as a metadata-only dataset to respect upstream licensing and is publicly available at Harvard Dataverse.

Furkan Yuceyalcin, Abdurrahim Yilmaz, Burak Temelkuran

Foundation models have transformed medical image analysis by providing robust feature representations that reduce the need for large-scale task-specific training. However, current benchmarks in dermatology often reduce the complex diagnostic taxonomy to flat, binary classification tasks, such as distinguishing melanoma from benign nevi. This oversimplification obscures a model's ability to perform fine-grained differential diagnoses, which is critical for clinical workflow integration. This study evaluates the utility of embeddings derived from ten foundation models, spanning general computer vision, general medical imaging, and dermatology-specific domains, for hierarchical skin lesion classification. Using the DERM12345 dataset, which comprises 40 lesion subclasses, we calculated frozen embeddings and trained lightweight adapter models using a five-fold cross-validation. We introduce a hierarchical evaluation framework that assesses performance across four levels of clinical granularity: 40 Subclasses, 15 Main Classes, 2 and 4 Superclasses, and Binary Malignancy. Our results reveal a "granularity gap" in model capabilities: MedImageInsights achieved the strongest overall performance (97.52% weighted F1-Score on Binary Malignancy detection) but declined to 65.50% on fine-grained 40-class subtype classification. Conversely, MedSigLip (69.79%) and dermatology-specific models (Derm Foundation and MONET) excelled at fine-grained 40-class subtype discrimination while achieving lower overall performance than MedImageInsights on broader classification tasks. Our findings suggest that while general medical foundation models are highly effective for high-level screening, specialized modeling strategies are necessary for the granular distinctions required in diagnostic support systems.

K. Liopyris, S. Gregoriou, Julia Dias et al.

Artificial intelligence (AI) based on machine learning and convolutional neuron networks (CNN) is rapidly becoming a realistic prospect in dermatology. Non-melanoma skin cancer is the most common cancer worldwide and melanoma is one of the deadliest forms of cancer. Dermoscopy has improved physicians’ diagnostic accuracy for skin cancer recognition but unfortunately it remains comparatively low. AI could provide invaluable aid in the early evaluation and diagnosis of skin cancer. In the last decade, there has been a breakthrough in new research and publications in the field of AI. Studies have shown that CNN algorithms can classify skin lesions from dermoscopic images with superior or at least equivalent performance compared to clinicians. Even though AI algorithms have shown very promising results for the diagnosis of skin cancer in reader studies, their generalizability and applicability in everyday clinical practice remain elusive. Herein we attempted to summarize the potential pitfalls and challenges of AI that were underlined in reader studies and pinpoint strategies to overcome limitations in future studies. Finally, we tried to analyze the advantages and opportunities that lay ahead for a better future for dermatology and patients, with the potential use of AI in our practices. Artificial intelligence (AI) is the development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, and translation between languages. The research on the use of AI in dermatology includes the ability of a machine to correctly classify a skin lesion. Skin cancer is the most common cancer worldwide and melanoma is the deadliest form of skin cancer. All skin cancers have a better prognosis when detected early in their development, hence their early detection is of paramount importance. Dermatologists use a dermatoscope—a specialized magnifying lens to improve their diagnostic capacity. However, even with the use of the dermatoscope, their ability to recognize skin cancer is far from perfect. AI has the ability to learn from dermoscopic images and subsequently provide an image-based diagnosis. Several studies compared the performance of machines and humans in classifying skin lesions from these images and showed that machines can classify skin lesions as good (and sometimes better) than humans. However, the application of AI in everyday clinical practice remains a challenge. In this article, we attempt to summarize the limitations and challenges that researchers found in their studies, and we provide strategies to improve the design of future studies. Finally, we describe the advantages and opportunities that could lay ahead for a better future for dermatology and patients.

Lauren Passby, N. Jenko, A. Wernham

ChatGPT is a large language model trained on increasingly large datasets by OpenAI to perform language-based tasks. It is capable of answering multiple-choice questions, such as those posed by the dermatology SCE examination. We asked two iterations of ChatGPT: ChatGPT-3.5 and ChatGPT-4 84 multiple-choice sample questions from the sample dermatology SCE question bank. ChatGPT-3.5 achieved an overall score of 63.1%, and ChatGPT-4 scored 90.5% (a significant improvement in performance (p<0.001)). The typical pass mark for the dermatology SCE is 70-72%. ChatGPT-4 is therefore capable of answering clinical questions and achieving a passing grade in these sample questions. There are many possible educational and clinical implications for increasingly advanced artificial intelligence (AI) and its use in medicine, including in the diagnosis of dermatological conditions. Such advances should be embraced provided that patient safety is a core tenet, and the limitations of AI in the nuances of complex clinical cases are recognised.

Miłosz Lewandowski, Paweł Łukowicz, D. Świetlik et al.

BACKGROUND The global use of artificial intelligence has the potential to revolutionize the healthcare industry. Despite the fact that artificial intelligence is becoming more popular, there is still a lack of evidence on its use in dermatology. OBJECTIVE The study aimed to determine the capacity of ChatGPT-3.5 and ChatGPT-4 to support dermatological knowledge and clinical decision-making in medical practice. METHODS Three dermatology specialty certificate tests, in English and Polish, consisting of 120 single-best-answer, multiple-choice format questions each, were used to assess ChatGPT-3.5 and ChatGPT-4 performance. RESULTS ChatGPT-4 exceeded the 60% pass rate in every performed test, with a minimum of 80% and 70% correct answers for the English and Polish versions, respectively. ChatGPT-4 performed significantly better on each exam (p<0.01), regardless of the language, compared to ChatGPT-3.5. Furthermore, ChatGPT-4 answered clinical picture-type questions with an average accuracy of 92.98% and 84.21% for English and Polish questions respectively. The difference between the tests in Polish and English did not turn out to be significant but still, ChatGPT-3.5 and ChatGPT-4 in English performed better overall than in Polish by an average of 8 percentage points for each test. Incorrect ChatGPT answers were highly correlated with a lower difficulty index, which denotes questions with higher difficulty in most of the tests. (p<0.05). CONCLUSION The dermatological knowledge level of ChatGPT was high, with a significantly better performance of ChatGPT-4 than ChatGPT-3.5. Although the use of ChatGPT will not replace the doctor's final decision, physicians should support artificial intelligence development in dermatology to raise the standards of medical care.

H. Jeong, Christine Park, Ricardo Henao et al.

Artificial intelligence (AI) has recently made great advances in image classification and malignancy prediction in the field of dermatology. However, understanding the applicability of AI in clinical dermatology practice remains challenging owing to the variability of models, image data, database characteristics, and variable outcome metrics. This systematic review aims to provide a comprehensive overview of dermatology literature using convolutional neural networks. Furthermore, the review summarizes the current landscape of image datasets, transfer learning approaches, challenges, and limitations within current AI literature and current regulatory pathways for approval of models as clinical decision support tools.

Choon Chiat Oh, Boon Yee Lim, Elizabeth Chun Yong Lee et al.

ABSTRACT Background Cutaneous squamous cell carcinoma (cSCC) remains poorly understood at the molecular level, both in the immunocompetent and immunosuppressed population with skin of colour. Data on the diversity of viruses found in cSCC is also lacking. Objectives We aimed to characterise the immunological and molecular profiles of cSCC in organ transplant recipients (OTR) and non‐transplant recipients in an Asian cohort (n = 53) and explore the diversity of viruses detected. Methods Gene expression analysis was performed on snap‐frozen cSCC tissues using the NanoString PanCancer IO360 panel. Viral detection was performed using the Twist Comprehensive Viral Research Panel. Results cSCC presented dysregulation of immune response pathways and tumour microenvironment remodelling compared to adjacent normal skin tissue. Cell‐type profiling based on gene expression profiles showed higher levels of exhausted CD8 cells, neutrophils, and cytotoxic cells in tumour cells. Furthermore, three distinct clusters of cSCC gene signatures could be observed, where Cluster 3 with the highest Tumour inflammation signature (TIS) scores displayed distinct upregulation of most pathways suggesting a more inflamed or “hot” tumour phenotype. cSCC of OTR exhibited greater expression of tumour markers (AQP9, SERPINA1) and reduced expression of T‐cell cytokines (CXCL10, CXCL11). Viruses were particularly enriched in tumour tissue, as compared with normal skin. In addition, there was an enrichment of detectable viruses in transplant‐associated cSCC, with several tumours harbouring multiple viruses (HPV, EBV, MCV, and TTV). Conclusions cSCC is marked by a pro‐tumorigenic immune environment with altered immune cell populations. These findings support the potential for stratified, immune‐tailored treatment approaches for cSCC, especially in OTR who have a higher disease burden. Future studies on the possible oncogenic role of the detected viruses can be undertaken.