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Within dermatology, Tik Tok content is both vast and highly visible, with dermatology-related hashtags collectively accumulating more than 18 billion views [2]. Sunscreen, a cornerstone of skin cancer prevention and photoaging mitigation, has emerged as a recurring theme across the platform, with #sunscreen alone garnering billions of views [3].
JMIR Dermatol 2025;8:e70010
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Evaluating Bias in Social Media Research Using #Sunscreen Content on Instagram Reels
In response to the increasing presence of dermatology-related content, dermatologists have been encouraged to engage in social media to decrease misinformation [1-4]. As dermatology-related content grows on social media, more research is being done to assess its quality [1]. Concerns have been raised about the reliability and reproducibility of social media research, particularly due to the personalized nature of platform algorithms [1].
JMIR Dermatol 2025;8:e76829
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Acral Persistent Papular Mucinosis in the United States: Case Series and Literature Review
dermatologyTraining of Health Professionals in Dermatology Dermatology
JMIR Dermatol 2025;8:e77714
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Dermatology, a medical specialty that relies almost exclusively on visual recognition and clinical pattern analysis, provides fertile ground for AI to revolutionize how medical students and aspiring dermatologists are trained [1]. Chatbots, such as Chat GPT, can play a transformative role in the medical education of dermatology. They can serve as on-demand tutors, providing instant explanations for complex dermatological terms, clarifying concepts, or answering questions in real time.
JMIR Dermatol 2025;8:e72058
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Readability of Online Patient Educational Materials for Rosacea: Systematic Web Search and Analysis
Further studies can evaluate patient comprehension of online dermatology patient educational material. Effective communication of medical knowledge to the general public is essential to bridging the readability gap. Readability should be facilitated through interactions between physicians and readers online and better physician understanding of patients’ health literacy needs.
JMIR Form Res 2025;9:e67916
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Deep Learning Multi-Modal Melanoma Detection: Algorithm Development and Validation
One relevant dermatological example used clinical features to represent the redness, flakiness, definite border extent, and other qualities to classify 6 types of erythemato-squamous skin diseases using the UCI Dermatology dataset [17]. Past tabular metadata for health applications have been used to diagnose other, nondermatological-related diseases. A Dual Bayesian Res Net50 model was used to train metadata regarding heart murmurs using XGBoost [18].
JMIR AI 2025;4:e66561
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Lewandowski et al [1] recently assessed Chat GPT-3.5 and Chat GPT-4’s performance in dermatology examinations, finding that Chat GPT-4 significantly outperformed its predecessor, achieving over a 60% pass rate overall and >84% accuracy on photo-based questions. Building on this, our study classified Chat GPT-4’s correctly answered question types using Bloom’s taxonomy for cognitive complexity [2].
JMIR Dermatol 2025;8:e74085
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GPT assistants have the potential to give pediatric dermatology patients and their families another modality for learning and asking questions about the conditions they face—one that is more understandable than Chat GPT alone. Furthermore, GPT assistants may enable clinicians to fine-tune information produced by a GPT specifically for their patient population.
JMIR Dermatol 2025;8:e59054
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Therefore, this study aimed to evaluate the effectiveness of a novel machine learning approach for predicting the optimal treatment for mild-to-moderate AV based on subjective patient self-assessment and objective measures, including the physician-rated Investigator Global Assessment (IGA) and the patient-rated Dermatology Life Quality Index (DLQI).
JMIR Dermatol 2025;8:e60883
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