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The diagnostic performance of convolutional neural networks (CNNs) for diagnosing several types of skin neoplasms has been demonstrated as comparable with that of dermatologists using clinical photography. However, the generalizability should be demonstrated using a large-scale external dataset that includes most types of skin neoplasms. In this study, the performance of a neural network algorithm was compared with that of dermatologists in both real-world practice and experimental settings. To demonstrate generalizability, the skin cancer detection algorithm (https://rcnn.modelderm.com) developed in our previous study was used without modification. We conducted a retrospective study with all single lesion biopsied cases (43 disorders; 40,331 clinical images from 10,426 cases: 1,222 malignant cases and 9,204 benign cases); mean age (standard deviation [SD], 52.1 [18.3]; 4,701 men [45.1%]) were obtained from the Department of Dermatology, Severance Hospital in Seoul, Korea between January 1, 2008 and March 31, 2019. Using the external validation dataset, the predictions of the algorithm were compared with the clinical diagnoses of 65 attending physicians who had recorded the clinical diagnoses with thorough examinations in real-world practice. In addition, the results obtained by the algorithm for the data of randomly selected batches of 30 patients were compared with those obtained by 44 dermatologists in experimental settings; the dermatologists were only provided with multiple images of each lesion, without clinical information. With regard to the determination of malignancy, the area under the curve (AUC) achieved by the algorithm was 0.863 (95% confidence interval [CI] 0.852-0.875), when unprocessed clinical photographs were used. The sensitivity and specificity of the algorithm at the predefined high-specificity threshold were 62.7% (95% CI 59.9-65.1) and 90.0% (95% CI 89.4-90.6), respectively. Furthermore, the sensitivity and specificity of the first clinical impression of 65 attending physicians were 70.2% and 95.6%, respectively, which were superior to those of the algorithm (McNemar test; p < 0.0001). The positive and negative predictive values of the algorithm were 45.4% (CI 43.7-47.3) and 94.8% (CI 94.4-95.2), respectively, whereas those of the first clinical impression were 68.1% and 96.0%, respectively. In the reader test conducted using images corresponding to batches of 30 patients, the sensitivity and specificity of the algorithm at the predefined threshold were 66.9% (95% CI 57.7-76.0) and 87.4% (95% CI 82.5-92.2), respectively. Furthermore, the sensitivity and specificity derived from the first impression of 44 of the participants were 65.8% (95% CI 55.7-75.9) and 85.7% (95% CI 82.4-88.9), respectively, which are values comparable with those of the algorithm (Wilcoxon signed-rank test; p = 0.607 and 0.097). Limitations of this study include the exclusive use of high-quality clinical photographs taken in hospitals and the lack of ethnic diversity in the study population. Our algorithm could diagnose skin tumors with nearly the same accuracy as a dermatologist when the diagnosis was performed solely with photographs. However, as a result of limited data relevancy, the performance was inferior to that of actual medical examination. To achieve more accurate predictive diagnoses, clinical information should be integrated with imaging information.

Background There has been a growing imbalance between supply of dermatologists and demand for dermatologic care. To best address physician shortages, it is important to delineate supply and demand patterns in the dermatologic workforce. The goal of this study was to explore dermatology supply and demand over time. Methods We conducted a cross-sectional analysis of workforce supply and demand projections for dermatologists from 2021 to 2036 using data from the Health Workforce Simulation Model from the National Center for Health Workforce Analysis. Estimates for total workforce supply and demand were summarized in aggregate and stratified by rurality. Scenarios with status quo demand and improved access were considered. Results Projected total supply showed a 12.45% increase by 2036. Total demand increased 12.70% by 2036 in the status quo scenario. In the improved access scenario, total supply was inadequate for total demand in any year, lagging by 28% in 2036. Metropolitan areas demonstrated a relative supply surplus up to 2036; nonmetropolitan areas had at least a 157% excess in demand throughout the study period. In 2021 adequacy was 108% and 39% adequacy for metropolitan and nonmetropolitan areas, respectively; these differences were projected to continue through 2036. Conclusions The findings suggest that the dermatology physician workforce is inadequate to meet the demand for dermatologic services in nonmetropolitan areas. Furthermore, improved access to dermatologic care would bolster demand and especially exacerbate workforce inadequacy in nonmetropolitan areas. Continued efforts are needed to address health inequities and ensure access to quality dermatologic care for all.

Cross-sectional study design is a type of observational study design. In a cross-sectional study, the investigator measures the outcome and the exposures in the study participants at the same time. Unlike in case-control studies (participants selected based on the outcome status) or cohort studies (participants selected based on the exposure status), the participants in a cross-sectional study are just selected based on the inclusion and exclusion criteria set for the study. Once the participants have been selected for the study, the investigator follows the study to assess the exposure and the outcomes. Cross-sectional designs are used for population-based surveys and to assess the prevalence of diseases in clinic-based samples. These studies can usually be conducted relatively faster and are inexpensive. They may be conducted either before planning a cohort study or a baseline in a cohort study. These types of designs will give us information about the prevalence of outcomes or exposures; this information will be useful for designing the cohort study. However, since this is a 1-time measurement of exposure and outcome, it is difficult to derive causal relationships from cross-sectional analysis. We can estimate the prevalence of disease in cross-sectional studies. Furthermore, we will also be able to estimate the odds ratios to study the association between exposure and the outcomes in this design.

Artificial intelligence (AI) systems have been shown to help dermatologists diagnose melanoma more accurately, however they lack transparency, hindering user acceptance. Explainable AI (XAI) methods can help to increase transparency, yet often lack precise, domain-specific explanations. Moreover, the impact of XAI methods on dermatologists’ decisions has not yet been evaluated. Building upon previous research, we introduce an XAI system that provides precise and domain-specific explanations alongside its differential diagnoses of melanomas and nevi. Through a three-phase study, we assess its impact on dermatologists’ diagnostic accuracy, diagnostic confidence, and trust in the XAI-support. Our results show strong alignment between XAI and dermatologist explanations. We also show that dermatologists’ confidence in their diagnoses, and their trust in the support system significantly increase with XAI compared to conventional AI. This study highlights dermatologists’ willingness to adopt such XAI systems, promoting future use in the clinic. Artificial intelligence has become popular as a cancer classification tool, but there is distrust of such systems due to their lack of transparency. Here, the authors develop an explainable AI system which produces text- and region-based explanations alongside its classifications which was assessed using clinicians’ diagnostic accuracy, diagnostic confidence, and their trust in the system.

Background According to the United States Census Bureau, the population aged ≥ 65 years is projected to increase, and the percentage of office visits from older adults to specialist physicians, such as dermatologists, will also increase. Despite older adults comprising an estimated 40% of dermatology visits currently, there is no formalized postgraduate curriculum on outpatient geriatric clinical care. Here, we performed an exploratory study to assess whether differences in the knowledge, attitudes and practices that are universal to outpatient geriatric clinical care exist among dermatologists compared to primary care or geriatric medicine, fields with formalized postgraduate curricula on outpatient geriatric clinical care. Methods Following Institutional Review Boards’ approvals, a voluntary, anonymized online survey was conducted in 2023 of faculty physicians from Dermatology, Primary Care (Internal Medicine/ Family Medicine) and Geriatric Medicine departments/divisions at three academic centers, Stanford University, University of California San Francisco and Emory University. Validated survey items were used whenever possible. All statistical tests applied to the results were adjusted for multiple comparisons. Results The overall response rate was 30.4% (146 completed surveys out of 480 invited). Knowledge of the 4Ms geriatric clinical framework was lowest amongst dermatologists (3.9%), followed by primary care physicians (17.8%) and geriatricians (100%). Attitudes on whether training program(s) provided adequate number of didactic lectures on clinical care of older adult patients indicated dermatologists responded with the lowest level of agreement (mean Likert score (MLS) 2.3, standard deviation (SD) 1.0) compared to primary care physicians’ responses (MLS 2.9, SD 1.1) and geriatricians (MLS 4.2, SD 1.4), p adjusted  < 0.05 for all comparisons. Practice differences in screening for mentation status were found, with dermatologists responding with lowest frequency (MLS 1.9, SD 1.2), followed by primary care physicians (MLS 3.7, SD 0.8), and geriatricians with highest frequency (MLS 4.5, SD 0.7), p adjusted  < 0.0001 for all comparisons. Conclusions Our data suggests that differences exist in the knowledge, attitudes and practices of outpatient clinical care of older adults in specialty fields (in this case dermatology) compared to primary care and geriatric medicine, and are actionable findings. Additional investigation into whether these differences are found in other specialized fields could inform future postgraduate training and research.

Psoriasis is a chronic immune-mediated inflammatory skin disease. Psoriasis lesions are characterized by hyper-proliferation of epidermal keratinocytes associated with inflammatory cellular infiltrate in both dermis and epidermis. The epidermis is the natural source of vitamin D synthesis by sunlight action. Recently, a role for vitamin D in the pathogenesis of different skin diseases, including psoriasis, has been reported. Indeed, significant associations between low vitamin D status and psoriasis have been systematically observed. Due to its role in proliferation and maturation of keratinocytes, vitamin D has become an important local therapeutic option in the treatment of psoriasis. To date, the successful treatment based on adequate dietary intake of vitamin D or oral vitamin D supplementation in psoriasis represent an unmet clinical need and the evidence of its beneficial effects remains still controversial. This information is important either for Dermatologists and Nutritionists to increases the knowledge on the possible bi-directional relationships between low vitamin D status and psoriasis and on the potential usefulness of vitamin D in psoriasis with the aim not only to reduce its clinical severity, but also for delineating the risk profile for co-morbidities cardiac risk factors that may result from psoriasis. In the current review, we analyzed the possible bi-directional links between psoriatic disease and vitamin D.

Scalp seborrheic dermatitis (SSD) is a common inflammatory condition requiring effective topical treatment options. To evaluate the efficacy and acceptability of a 1% selenium disulfide (SeS2)-0.9% salicylic acid shampoo in treating mild to moderate SSD among dermatologists. 95 dermatologists with mild to moderate SSD used the shampoo thrice weekly for 4 weeks. Symptoms were assessed using Visual Analog Scale (VAS) at baseline, Day 14, and Day 28. After 4-week treatment, severe dandruff cases decreased from 28.4% to 3.2%, with 90.5% participants reporting only mild or no dandruff. VAS scores showed significant improvement in all symptoms ( p  < 0.01). Product satisfaction reached 88.5%, with 90.5% willing to recommend it clinically. The findings of this study suggest that a shampoo based on 1% selenium disulfide (SeS2) and 0.9% salicylic acid is effective in helping alleviate the symptoms of seborrheic dermatitis (SSD).

Background Internationally, teledermatology has proven to be a viable alternative to conventional physical referrals. Travel cost and referral times are reduced while patient safety is preserved. Especially patients from rural areas benefit from this healthcare innovation. Despite these established facts and positive experiences from EU neighboring countries like the Netherlands or the United Kingdom, Germany has not yet implemented store-and-forward teledermatology in routine care. Methods The TeleDerm study will implement and evaluate store-and-forward teledermatology in 50 general practitioner (GP) practices as an alternative to conventional referrals. TeleDerm aims to confirm that the possibility of store-and-forward teledermatology in GP practices is going to lead to a 15% ( n  = 260) reduction in referrals in the intervention arm. The study uses a cluster-randomized controlled trial design. Randomization is planned for the cluster “county”. The main observational unit is the GP practice. Poisson distribution of referrals is assumed. The evaluation of secondary outcomes like acceptance, enablers and barriers uses a mixed-methods design with questionnaires and interviews. Discussion Due to the heterogeneity of GP practice organization, patient management software, information technology service providers, GP personal technical affinity and training, we expect several challenges in implementing teledermatology in German GP routine care. Therefore, we plan to recruit 30% more GPs than required by the power calculation. The implementation design and accompanying evaluation is expected to deliver vital insights into the specifics of implementing telemedicine in German routine care. Trial registration German Clinical Trials Register, DRKS00012944 . Registered prospectively on 31 August 2017.

The purpose of this study was to evaluate dermatologists’ knowledge, attitudes, and practices (KAP) concerning omalizumab therapy for chronic urticaria. We conducted a cross-sectional study in several hospitals in China, mainly in hospitals in Zhejiang Province, during August 1, 2024 - August 15, 2024 using a self-administered KAP questionnaire. Wilcoxon-Mann-Whitney tests and Kruskal-Wallis analysis of variance were performed to compare differences across groups Factors influencing practice were determined through multivariable logistic regression. The study analyzed 354 valid questionnaires. Among the respondents, 248 (70.06%) were aged between 31 and 35 years, and 269 (75.99%) had 5–10 years of experience. The mean scores for knowledge, attitudes, and practices were 5.71 ± 1.39 (possible range: 0–9), 21.39 ± 2.25 (possible range: 5–25), and 19.84 ± 2.53 (possible range: 5–25), respectively. Multivariate logistic regression indicated that a higher attitude score (OR = 1.929, 95% CI: [1.511–2.462], P  < 0.001), being male (OR = 3.262, 95% CI: [1.507–7.059], P  = 0.003), and awareness of omalizumab (OR = 4.966, 95% CI: [1.466–16.830], P  = 0.010) were significantly associated with proactive practice. Dermatologists demonstrated insufficient knowledge, positive attitudes, and proactive practices towards omalizumab therapy for chronic urticaria. Given the identified knowledge gap, there is a pressing need for targeted educational interventions to enhance dermatologists’ understanding of omalizumab therapy for chronic urticaria, aiming to improve patient care and outcomes in clinical practice.

Artificial intelligence (AI)-enabled decision support systems are critical tools in medical practice; however, their reliability is not absolute, necessitating human oversight for final decision-making. Human reliance on such systems can vary, influenced by factors such as individual psychological factors and physician experience. This study aimed to explore the psychological factors influencing subjective trust and reliance on medical AI's advice, specifically examining relative AI reliance and relative self-reliance to assess the appropriateness of reliance. A survey was conducted with 223 dermatologists, which included lesion image classification tasks and validated questionnaires assessing subjective trust, propensity to trust technology, affinity for technology interaction, control beliefs, need for cognition, as well as queries on medical experience and decision confidence. A 2-tailed t test revealed that participants' accuracy improved significantly with AI support (t =-3.3; P<.001; Cohen d=4.5), but only by an average of 1% (1/100). Reliance on AI was stronger for correct advice than for incorrect advice (t =4.2; P<.001; Cohen d=0.1). Notably, participants demonstrated a mean relative AI reliance of 10.04% (139/1384) and a relative self-reliance of 85.6% (487/569), indicating a high level of self-reliance but a low level of AI reliance. Propensity to trust technology influenced AI reliance, mediated by trust (indirect effect=0.024, 95% CI 0.008-0.042; P<.001), and medical experience negatively predicted AI reliance (indirect effect=-0.001, 95% CI -0.002 to -0.001; P<.001). The findings highlight the need to design AI support systems in a way that assists less experienced users with a high propensity to trust technology to identify potential AI errors, while encouraging experienced physicians to actively engage with system recommendations and potentially reassess initial decisions.