Climate-driven increases in temperature, air pollution, UV radiation, and extreme weather (including floods and storms) harm the skin through direct and indirect mechanisms, causing exacerbations of common and rarer infl...Climate-driven increases in temperature, air pollution, UV radiation, and extreme weather (including floods and storms) harm the skin through direct and indirect mechanisms, causing exacerbations of common and rarer inflammatory skin disorders. Here, we comprehensively review and summarize studies that assess the effect of climatic factors on psoriasis, acne, seborrheic dermatitis, and lupus.
Artificial intelligence (AI) is increasingly being used in dermatology, particularly for image-based diagnostics. However, a number of regulatory and legal challenges remains unanswered. This article reviews the UK regul...Artificial intelligence (AI) is increasingly being used in dermatology, particularly for image-based diagnostics. However, a number of regulatory and legal challenges remains unanswered. This article reviews the UK regulations for AI-driven medical devices with reference to international comparisons. We have also highlighted some liability concerns with clinical examples to illustrate the complexity. As AI deployment continues to rapidly expand, it is critical that health care practitioners ensure not only that these technologies are implemented with patient safety and ethical considerations at the forefront but that they are also aware of the importance of regulatory compliance and liability issues.
The rise of advanced transformer-based generative artificial intelligence models represents one of the most profound changes to the medical landscape of the past century. Given the potential to impact everything from med...The rise of advanced transformer-based generative artificial intelligence models represents one of the most profound changes to the medical landscape of the past century. Given the potential to impact everything from medical education to surgical decision-making, it is important to appreciate fundamental aspects of how these systems work and understand the ways in which they can be used responsibly to improve patient care. Prompt engineering provides the most obvious opportunity for clinicians to tailor these systems to their needs, but appreciating possible risks, including direct and indirect effects, is of profound importance for the clinician, educator, and researcher.
This article explores the transformative role of artificial intelligence (AI) in dermatology education, emphasizing the need for dermatologists to develop AI literacy alongside clinical expertise. It reviews AI's capabil...This article explores the transformative role of artificial intelligence (AI) in dermatology education, emphasizing the need for dermatologists to develop AI literacy alongside clinical expertise. It reviews AI's capabilities and limitations, as well as strategies for evaluating research, industry partnerships, and clinical applications. Additionally, it provides guidance on navigating AI use by patients and offers resources to help patients engage safely with these technologies. This article aims to prepare dermatologists for the AI era and to keep pace with the evolving landscape of AI-enabled dermatology and learning health systems.
Although most applications of artificial intelligence (AI) in dermatology to date have focused on diagnostic use cases (eg, computer vision algorithms for the diagnosis of pigmented skin lesions), the advent of highly so...Although most applications of artificial intelligence (AI) in dermatology to date have focused on diagnostic use cases (eg, computer vision algorithms for the diagnosis of pigmented skin lesions), the advent of highly sophisticated generative AI algorithms may potentially enable more creative use cases for AI. In cosmetic and procedural dermatology, AI has begun to be used by consumers and dermatologists to visualize treatments ahead of time or highlight opportunities for improvement.
The intersection of dermatology and drug discovery represents a dynamic field of research aimed at addressing the growing burden of skin-related diseases. Recent advances in molecular biology, genomics, and pharmacology...The intersection of dermatology and drug discovery represents a dynamic field of research aimed at addressing the growing burden of skin-related diseases. Recent advances in molecular biology, genomics, and pharmacology offer improved outcomes for patients. This review highlights the current state of drug discovery in dermatology, focusing on identification of new therapeutic targets, role of personalized medicine, and application of artificial intelligence in drug development. By providing an overview of the latest innovations, challenges, and future directions, this review aims to inform and inspire researchers and physicians in the quest for more effective therapies utilizing emerging technologies for dermatologic diseases.
Artificial intelligence (AI) has the potential to revolutionize the field of medicine including dermatopathology. A rapidly growing body of research supports a promising role for AI in various aspects of dermatopathology...Artificial intelligence (AI) has the potential to revolutionize the field of medicine including dermatopathology. A rapidly growing body of research supports a promising role for AI in various aspects of dermatopathology, particularly when used in conjunction with human pathologists. This article reviews the current state of AI in dermatopathology, its use in the diagnosis and prognostication of cutaneous diseases (particularly skin cancer, a focus within the field), as well as the challenges and ethical considerations critical for the integration of AI into clinical practice.
Teledermatology has transformed access to quality dermatology care. Meanwhile, artificial intelligence (AI)-driven solutions enhance diagnostic accuracy, triage, and decision support in study settings. Integrating AI and...Teledermatology has transformed access to quality dermatology care. Meanwhile, artificial intelligence (AI)-driven solutions enhance diagnostic accuracy, triage, and decision support in study settings. Integrating AI and teledermatology can streamline workflows, expedite detection of malignant lesions, standardize care, and enable cost-effective resource allocation. Despite operational, technical, and ethical hurdles including data privacy, algorithmic bias, and limited regulatory clarity, ongoing research, advancing technology and collaborative implementation promise improved accuracy, interoperability, and equitable adoption for AI-augmented dermatologic care worldwide.
Deep learning is revolutionizing dermatology, enabling accurate diagnosis of skin lesions, particularly melanoma. Early research demonstrated its potential, but limitations in training data hindered real-world applicatio...Deep learning is revolutionizing dermatology, enabling accurate diagnosis of skin lesions, particularly melanoma. Early research demonstrated its potential, but limitations in training data hindered real-world application. Recent advances include diverse datasets and integration with noninvasive imaging techniques, leading to artificial intelligence-powered tools for clinical use. Challenges remain, including the need for robust validation methods, addressing biases, and ensuring patient safety through postmarket surveillance. Foundation models hold promise for future development but require careful consideration of ethical and practical implications. Collaboration between stakeholders is crucial to successfully integrate this transformative technology and improve patient outcomes.
The rapid use of artificial intelligence (AI) in dermatology raises important concerns around bias, privacy, and the need for strong regulation to ensure ethical implementation. Trust in AI depends on its accuracy, trans...The rapid use of artificial intelligence (AI) in dermatology raises important concerns around bias, privacy, and the need for strong regulation to ensure ethical implementation. Trust in AI depends on its accuracy, transparency, and proper use, while physicians must continue to apply their clinical judgment. AI systems should aim to minimize bias and increase transparency, recognizing that perfection in these areas is unlikely but should be pursued as much as possible. Access to AI tools must be equitable, with special attention to underserved and rural populations to prevent widening health care disparities.
Artificial intelligence (AI) has the potential to help address critical challenges in dermatology care, including a shortage of dermatologists and increasing patient demand. This review explores 3 primary areas of AI-ena...Artificial intelligence (AI) has the potential to help address critical challenges in dermatology care, including a shortage of dermatologists and increasing patient demand. This review explores 3 primary areas of AI-enabled diagnostic tools in dermatological diseases: skin cancers, non-cancer dermatological conditions, and supplemental imaging tools. Some AI models have demonstrated accuracy, sensitivity, and specificity comparable to dermatologists in detecting melanoma, basal cell carcinoma, and squamous cell carcinoma, as well as common inflammatory and autoimmune conditions such as psoriasis, eczema, and acne. Integration with supplemental imaging modalities, including dermoscopy, optical coherence tomography, and reflectance confocal microscopy, have also shown diagnostic accuracy.
Recent developments in artificial intelligence (AI) have the potential to revolutionize dermatology by improving patient-care and reducing administrative burden. We discuss current and future applications of AI in dermat...Recent developments in artificial intelligence (AI) have the potential to revolutionize dermatology by improving patient-care and reducing administrative burden. We discuss current and future applications of AI in dermatology that can enhance clinical decision-making, diagnostic and prognostic processes, and health care operations. We will explore AI-driven imaging tools for dermatologists and dermatopathologists, multimodal AI techniques enabling precision medicine, and generative AI systems that support clinical practice. While further refinements are needed for widespread implementation, AI-based applications present an opportunity for dermatologists to improve patient-care and minimize resource demands.
Metastatic melanoma is an aggressive and treatment-resistant skin cancer with a low 5-year survival rate of 27%. Historically considered radioresistant, melanoma's response to radiation therapy (RT) has evolved, especial...Metastatic melanoma is an aggressive and treatment-resistant skin cancer with a low 5-year survival rate of 27%. Historically considered radioresistant, melanoma's response to radiation therapy (RT) has evolved, especially when integrated with systemic therapies like immune checkpoint inhibitors (ICIs). RT is now recognized for its utility in local control, palliative care, and brain metastasis management. Emerging evidence shows RT's synergy with ICIs through mechanisms like the abscopal effect. This article explores RT's evolving role in metastatic melanoma treatment, focusing on integration with modern therapies and ongoing research into optimizing outcomes.
Systemic therapies for advanced local and metastatic melanoma, typically used for Stage IIB or higher, can broadly be classified into 2 categories: immunotherapies and targeted therapies. Immunotherapies work by inhibiti...Systemic therapies for advanced local and metastatic melanoma, typically used for Stage IIB or higher, can broadly be classified into 2 categories: immunotherapies and targeted therapies. Immunotherapies work by inhibiting immune checkpoints (ie, disinhibiting immune checkpoint blockade), resulting in greater T-cell anti-tumor activity. Targeted therapies, reserved for BRAF V600-mutant melanomas, are orally-available small molecules that inhibit the function of activated oncoproteins such as BRAF and MEK. These systemic medications carry a significant risk for adverse effects, often affecting the cutaneous, gastrointestinal and endocrine systems.
Mohs micrographic surgery (MMS) is a tissue-sparing surgical technique that is the standard of care for treatment of several cutaneous malignancies. Current US and international guidelines recommend wide local excision a...Mohs micrographic surgery (MMS) is a tissue-sparing surgical technique that is the standard of care for treatment of several cutaneous malignancies. Current US and international guidelines recommend wide local excision as the first-line surgical therapy for noninvasive melanoma, and use of MMS may be appropriate for melanoma-in-situ, lentigo maligna, and potentially thin invasive malignant melanoma. Based on available literature, MMS can potentially result in lower recurrence rates of melanoma, especially when using immunostaining. This chapter explores the existing evidence supporting MMS for treatment of melanoma as well as its challenges.
Cutaneous melanoma is a highly aggressive malignancy originating from melanocytes. It frequently occurs on the head/neck where it is often diagnosed at advanced stages and associated with poorer outcomes compared to the...Cutaneous melanoma is a highly aggressive malignancy originating from melanocytes. It frequently occurs on the head/neck where it is often diagnosed at advanced stages and associated with poorer outcomes compared to the trunk and extremities. Wide local excision remains the standard-of-care for localized disease; however, Mohs micrographic surgery offers superior local control for melanomas in cosmetically sensitive areas. Sentinel lymph node biopsy remains essential for staging. Recent advances in adjuvant and neoadjuvant therapies have improved survival, while innovations in genetic profiling, prediction tools, and tailored imaging are enhancing personalized treatment, accurate staging and detection, resulting in better patient outcomes.
Genomic advancements have transformed melanoma prognosis by identifying key genetic alterations that influence disease progression and treatment outcomes. Gene expression profiling (GEP) tests, including the 31-GEP, 11-G...Genomic advancements have transformed melanoma prognosis by identifying key genetic alterations that influence disease progression and treatment outcomes. Gene expression profiling (GEP) tests, including the 31-GEP, 11-GEP, and 8-GEP + CP, refine traditional staging by stratifying patients based on recurrence and metastasis risk. These tests enhance clinical decision-making by guiding sentinel lymph node biopsy selection, surveillance intensity, and adjuvant therapy use. Studies confirm their prognostic accuracy, linking GEP results to survival outcomes. Despite their potential, challenges like cost and validation limit widespread adoption. As research progresses, integrating genomic data with traditional staging could further personalize melanoma management.
The eighth edition of the AJCC Cancer Staging Manual introduced important updates to melanoma staging to improve prognostic accuracy and guide treatment. However, it has some limitations. Genetic testing, nomograms, and...The eighth edition of the AJCC Cancer Staging Manual introduced important updates to melanoma staging to improve prognostic accuracy and guide treatment. However, it has some limitations. Genetic testing, nomograms, and biobanks are becoming important tools for better risk assessment and personalized treatment. While imaging advances are promising, further research is needed to determine if they can replace traditional methods like sentinel lymph node biopsy. Overall, the eighth edition is a step forward, but further improvements and a focus on personalized approaches are needed for better patient outcomes.