JMIR dermatology最新文献

{"title":"Representation of Psoriasis on the Web for Patients With Skin of Color.","authors":"Daniel Nguyen, Van Le, Derek Nguyen, Vy Han","doi":"10.2196/69026","DOIUrl":"10.2196/69026","url":null,"abstract":"<p><strong>Unlabelled: </strong>This study analyzed over 2000 images of psoriasis across major web-based platforms and found a significant underrepresentation of darker skin tones, highlighting a critical gap in dermatologic representation that may contribute to misdiagnoses and health disparities among patients with skin of color.</p>","PeriodicalId":73553,"journal":{"name":"JMIR dermatology","volume":"8 ","pages":"e69026"},"PeriodicalIF":0.0,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12326156/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144790852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patient Perceptions of Artificial Intelligence and Telemedicine in Dermatology: A Narrative Review.","authors":"Charlotte McRae, Ting Dan Zhang, Leslie Donoghue Seeley, Michael Anderson, Laci Turner, Lauren V Graham","doi":"10.2196/75454","DOIUrl":"10.2196/75454","url":null,"abstract":"<p><strong>Background: </strong>Artificial intelligence (AI) and telemedicine have great potential to transform dermatology care delivery, but patient perspectives on these technologies have not been systematically compared.</p><p><strong>Objective: </strong>To examine patient perspectives on AI and telemedicine in dermatology to inform implementation strategies as these technologies increasingly converge in clinical practice.</p><p><strong>Methods: </strong>A comprehensive literature search was conducted using PubMed, Scopus, and Embase databases between August 2024 and October 2024. We identified 48 articles addressing patient perspectives on AI and telemedicine in dermatology, with none directly comparing views on both technologies.</p><p><strong>Results: </strong>Several distinct themes emerged regarding patient perspectives on these technologies: willingness to use, perceived benefits and risks, barriers to implementation, and conditions necessary for successful integration. Findings revealed that patients express hesitancy towards AI-based diagnoses that lack dermatologist involvement, while preferences for teledermatology varied by appointment reason, age, and prior technology exposure. Patients' motivations for AI implementation are connected to AI's potential for quicker diagnoses and improved triage efficiency, while telemedicine addresses logistical challenges such as reduced travel time and improved appointment availability. Both technologies were perceived to improve accessibility and diagnostic efficiency, though patients expressed concerns about AI's limited communication abilities and teledermatology's limits in performing physical examinations. Primary adoption barriers for these modalities included technological limitations and trust concerns, with patients emphasizing the need for dermatologist oversight, transparency, and adequate educational resources for successful integration.</p><p><strong>Conclusions: </strong>The complementary strengths of AI and teledermatology suggest they could mitigate each other's limitations when integrated-AI potentially enhancing teledermatology's diagnostic accuracy while teledermatology addresses AI's lack of human connection. By thoroughly examining these perspectives, this review may serve as a guide for patient-centered technological integration in the future landscape of accessible dermatologic care.</p>","PeriodicalId":73553,"journal":{"name":"JMIR dermatology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12440257/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144859971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating the Readability of Pediatric Neurocutaneous Syndromes-Related Patient Education Material Created by a Custom GPT With Retrieval Augmentation.","authors":"Nneka Ede, Robyn Okereke","doi":"10.2196/59054","DOIUrl":"10.2196/59054","url":null,"abstract":"<p><strong>Unlabelled: </strong>In our study, we developed a GPT assistant with a custom knowledge base for neurocutaneous diseases, tested its ability to answer common patient questions, and showed that a GPT using retrieval augmentation generation can improve the readability of patient educational material without being prompted for a specific reading level.</p>","PeriodicalId":73553,"journal":{"name":"JMIR dermatology","volume":"8 ","pages":"e59054"},"PeriodicalIF":0.0,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12286582/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144651331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effectiveness of a Machine Learning-Enabled Skincare Recommendation for Mild-to-Moderate Acne Vulgaris: 8-Week Evaluator-Blinded Randomized Controlled Trial.","authors":"Misbah Noshela Ghazanfar, Ali Al-Mousawi, Christian Riemer, Benóný Þór Björnsson, Charlotte Boissard, Ivy Lee, Zarqa Ali, Simon Francis Thomsen","doi":"10.2196/60883","DOIUrl":"10.2196/60883","url":null,"abstract":"<p><strong>Background: </strong>Acne vulgaris (AV) is one of the most common skin disorders, with a peak incidence in adolescence and early adulthood. Topical treatments are usually used for mild to moderate AV; however, a lack of adherence to topical treatment is seen in patients due to various reasons. Therefore, personalized skincare recommendations may be beneficial for treating mild-to-moderate AV.</p><p><strong>Objective: </strong>This study aimed to evaluate the effectiveness of a novel machine learning approach in predicting the optimal treatment for mild-to-moderate AV based on self-assessment and objective measures.</p><p><strong>Methods: </strong>A randomized, evaluator-blinded, parallel-group study was conducted on 100 patients recruited from an internet-based database and randomized in a 1:1 ratio (groups A and B) based on their consent form submission. Groups A and B received customized product recommendations using a Bayesian machine learning model and self-selected treatments, respectively. The patients submitted self-assessed disease scores and photographs after the 8-week treatment. The primary and secondary outcomes were photograph evaluation by two board-certified dermatologists using the Investigator Global Assessment (IGA) scores and quality of life (QoL) measured using the Dermatology Life Quality Index (DLQI), respectively.</p><p><strong>Results: </strong>Overall, 99 patients were screened, and 68 patients (mean age: 27 years, SD 4.56 years) were randomized into groups A (customized) and B (self-selected). IGA scores significantly improved after treatment in group A but not in group B (mean difference in IGA score; group A=0.32, P=.04 vs group B=0.09, P=.54). The DLQI significantly improved in group A from 7.75 at baseline to 3.5 (P<.001) after treatment but reduced in group B from 7.53 to 5.3 (P>.05). IGA scores and the DLQI were significantly correlated in group A, but not in group B. A total of 3 patients reported adverse reactions in group B, but none in group A.</p><p><strong>Conclusions: </strong>Using a machine learning model for personalized skincare recommendations significantly reduced symptoms and improved severity and overall QoL of patients with mild-to-moderate AV, supporting the potential of machine learning-based personalized treatment options in dermatology.</p>","PeriodicalId":73553,"journal":{"name":"JMIR dermatology","volume":"8 ","pages":"e60883"},"PeriodicalIF":0.0,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12310563/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144651330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring Attention-Deficit/Hyperactivity Disorder Symptoms in Patients With Atopic Dermatitis by Disease Severity: Cross-Sectional Analysis.","authors":"Amr Molla, Raed Jannadi, Dareen Hafez, Lujain Alrohaily, Ebtesam Abdullah, Duha Azouni, Muayad Albadrani","doi":"10.2196/74126","DOIUrl":"10.2196/74126","url":null,"abstract":"<p><strong>Background: </strong>Atopic dermatitis (AD) is a chronic inflammatory skin condition affecting a significant percentage of the global population. Emerging research suggests a potential link between AD and neurodevelopmental disorders like attention-deficit/hyperactivity disorder (ADHD). However, there is a lack of comprehensive studies within the Saudi Arabian population examining this association.</p><p><strong>Objective: </strong>This study aims to determine the prevalence of ADHD among patients with AD in Saudi Arabia and to explore potential associations with demographic and clinical factors.</p><p><strong>Methods: </strong>In this cross-sectional, multicenter study conducted between May and November 2024, 419 patients with AD were recruited from various hospitals in Saudi Arabia. Children were screened for ADHD symptoms using the ADHD Rating Scale-5, while adults were assessed with the Adult Self-Report Scale. Logistic regression was used to evaluate the influence of AD severity, age, gender, nationality, and BMI on the likelihood of ADHD symptoms.</p><p><strong>Results: </strong>A total of 419 patients with AD were included, of whom 234 (55.8%) were children and 185 (44.2%) were adults; 239 (57%) were female and 360 (85.9%) were Saudi nationals. ADHD symptoms were identified in 84 (20%) patients, with a slightly higher prevalence among children (49/234, 20.9%) compared to adults (35/185, 18.9%; P=.61). No significant associations were found between ADHD symptoms and gender, nationality, BMI, or AD severity in either age group. Moderate to severe AD was more common among adults (48/185, 25.9%) than children (42/234, 17.9%; P=.048).</p><p><strong>Conclusions: </strong>This study found that 20% of patients with AD screened positive for ADHD symptoms, with slightly higher rates in children than adults. No significant associations were observed between ADHD symptoms and gender, nationality, BMI, or AD severity. Although no significant clinical predictors were identified, the findings emphasize the need for ADHD screening in patients with AD, particularly in regions with high AD prevalence. Future longitudinal studies should explore underlying mechanisms and assess how managing one condition may influence the other.</p>","PeriodicalId":73553,"journal":{"name":"JMIR dermatology","volume":"8 ","pages":"e74126"},"PeriodicalIF":0.0,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12283059/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144644314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the Accuracy of ChatGPT in Appropriately Triaging Common Postoperative Concerns Regarding Mohs Micrographic Surgery.","authors":"Chloe Fernandez, Victoria Dukharan, Nathaniel A Marroquin, Rebecca Bolen, Adam Leavitt, Nicole C Cabbad","doi":"10.2196/72706","DOIUrl":"10.2196/72706","url":null,"abstract":"<p><strong>Unlabelled: </strong>Artificial intelligence (AI) is increasingly integrated into health care, offering potential benefits in patient education, triage, and administrative efficiency. This study evaluates AI-driven dialogue interfaces within an electronic health record and patient portal system for postoperative care in Mohs micrographic surgery.</p>","PeriodicalId":73553,"journal":{"name":"JMIR dermatology","volume":"8 ","pages":"e72706"},"PeriodicalIF":0.0,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12262145/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144593086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rising Leishmaniasis Cases in the United States Based on Registry Data From 2007 to 2023 and the Vital Role of Health Care Providers in Awareness and Management.","authors":"Mia Panlilio, Olnita Martini, Elizabeth Tchernogorova, Alexa Carboni, Danielle Duffle, Leslie Torgerson","doi":"10.2196/65579","DOIUrl":"10.2196/65579","url":null,"abstract":"<p><strong>Unlabelled: </strong>This letter highlights the increasing incidence of leishmaniasis cases in the United States, using the available data from Texas, and underscores the need for heightened awareness among health care providers regarding leishmaniasis diagnosis and prevention.</p>","PeriodicalId":73553,"journal":{"name":"JMIR dermatology","volume":"8 ","pages":"e65579"},"PeriodicalIF":0.0,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12204042/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144334583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Influence of Popular Media on Public Interest in Red-Light Therapy: Longitudinal Trend Analysis.","authors":"Catherine Z Shen, Aaron T Zhao","doi":"10.2196/69796","DOIUrl":"10.2196/69796","url":null,"abstract":"<p><strong>Unlabelled: </strong>TikTok's influence has significantly increased public interest in red-light therapy, surpassing that for traditional skin care treatments; this highlights the powerful role of social media in shaping health care trends and underscores the need for health care providers to stay informed about viral social media trends on treatment.</p>","PeriodicalId":73553,"journal":{"name":"JMIR dermatology","volume":"8 ","pages":"e69796"},"PeriodicalIF":0.0,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12178215/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144287458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating Experiences With Scarring Among Transgender and Gender Diverse People: Mixed Methods Study.","authors":"Nora Yanyi Sun, Kanika Kamal, Alex Sogomon Keuroghlian","doi":"10.2196/62714","DOIUrl":"10.2196/62714","url":null,"abstract":"<p><strong>Background: </strong>Scarring has been shown to have adverse health effects on marginalized patient groups. However, experiences of scarring among transgender and gender diverse (TGD) people have not yet been thoroughly characterized. .</p><p><strong>Objective: </strong>This study aimed to investigate the impacts of scarring related to gender-affirming care and other causes among TGD people.</p><p><strong>Methods: </strong>Anonymous data were extracted from Reddit, a popular online platform organized into \"subreddit\" groups based on identities and interests. A combined total of 604 posts and comments that explicitly reference physical scarring were extracted from r/FtM, a subreddit for transmasculine people (449 posts and comments) and r/MtF, a subreddit for transfeminine people (155 posts and comments). Applying inductive thematic analysis, all posts and comments were coded and codes were sorted into overarching themes. .</p><p><strong>Results: </strong>Among the 604 posts and comments, the scars most discussed were secondary to gender-affirming care procedures, including mastectomy (n=338 posts and comments), hormone administration (n=102 posts and comments), and hair removal (n=38 posts and comments). Nongender-affirming care-related scars, such as those due to self-harm (n=43 posts and comments), were discussed less often. A total of five overarching themes emerged through thematic analysis: (1) concerns about physical outcomes related to scarring; (2) psychological distress related to scarring; (3) societal perceptions of scarring; (4) strategies to prevent, conceal, and minimize scarring; and (5) positive experiences with scarring.</p><p><strong>Conclusions: </strong>For TGD people, scar complications, visibility, and permanence represent major concerns. While many TGD people ultimately accept scarring as an unavoidable consequence, scarring both related and unrelated to gender-affirming care can present a significant psychosocial stressor for TGD people. Scarring can result in physical health complications, gender dysphoria, and negative body image; visible scarring is also a barrier for TGD people who wish to blend into society. Clinicians should improve communication regarding scarring outcomes and scar-care procedures. Future research should focus on the development of scar prevention, care, and reduction techniques for TGD people.</p>","PeriodicalId":73553,"journal":{"name":"JMIR dermatology","volume":"8 ","pages":"e62714"},"PeriodicalIF":0.0,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12164948/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144251110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence in Patch Testing: Comprehensive Review of Current Applications and Future Prospects in Dermatology.","authors":"Hilary S Tang, Joseph Ebriani, Matthew J Yan, Shannon Wongvibulsin, Mehdi Farshchian","doi":"10.2196/67154","DOIUrl":"10.2196/67154","url":null,"abstract":"<p><strong>Background: </strong>The integration of artificial intelligence (AI) into patch testing for allergic contact dermatitis (ACD) holds the potential to standardize diagnoses, reduce interobserver variability, and improve overall diagnostic accuracy. However, the challenges and limitations hindering clinical implementation have not been thoroughly explored.</p><p><strong>Objective: </strong>This narrative review aims to examine the current applications of AI in patch testing, identify challenges, and propose future directions for their use in dermatology.</p><p><strong>Methods: </strong>PubMed was searched in August 2024 to identify studies involving human participants undergoing patch testing with AI used in the study. Exclusion criteria were non-English and nonoriginal research. Data were synthesized to assess study design, performance, and potential for clinical application.</p><p><strong>Results: </strong>Out of 94 reviewed articles, 10 met the inclusion criteria. Most studies employed convolutional neural networks (CNN) for image analysis, with accuracy rates ranging from 90.1% to 99.5%. Other AI models, such as gradient boosting and random forest, were used for risk prediction and biomarker discovery. Key limitations included limited sample sizes, variability in image capture protocols, and lack of standardized reporting on skin types.</p><p><strong>Conclusions: </strong>AI has significant potential to enhance diagnostic accuracy, standardize patch test interpretation, and expand access to patch testing. However, standardized imaging protocols, larger and more diverse datasets, and improved regulatory frameworks are necessary to realize the full potential of AI in patch testing.</p>","PeriodicalId":73553,"journal":{"name":"JMIR dermatology","volume":"8 ","pages":"e67154"},"PeriodicalIF":0.0,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12178223/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144210415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}