BACKGROUND AND OBJECTIVES:Artificial intelligence was shown to improve diagnostic accuracy for skin cancer detection. While most clinically approved models provide binary "benign/malignant" classifications, multiclass predictions may offer greater clinical utility. Yet, comparisons between multiclass convolutional neural networks (CNNs) and dermatologists are scarce. METHODS:In an international web-based reader study, dermatologists (n = 96) and a prototype multiclass CNN (FotoFinder Systems, Germany) assigned diagnoses to 100 skin lesions by using nine disease categories (melanoma; basal cell carcinoma; squamous cell carcinoma; intraepithelial carcinoma; melanocytic nevus; benign keratinocytic lesion; dermatofibroma; vascular lesion; "other"). The main outcome measure was overall mean sensitivity (micro-averaged percentage of correct diagnosis) of dermatologists versus multiclass CNN. RESULTS:Dermatologists achieved an overall mean sensitivity (95% CI) of 69.4% (68.4%-70.3%) with dermatoscopy-only data (level-I), which improved to 76.0% (75.2%-76.9%) when provided with full clinical information (level-II). The CNN's top-rank predictions showed a higher overall mean sensitivity of 82.0% (73.3%-88.3%). The CNN significantly outperformed all dermatologist subgroups, except for dermatoscopy experts at study level-II (overall mean sensitivity 81.1% [79.9%-82.3%], pequivalence = 0.0085). CONCLUSIONS:Multiclass CNN predictions outperformed most dermatologists in diagnostic accuracy, supporting their potential to enhance clinical decision-making, particularly in settings with limited dermatological expertise.

BACKGROUND:Dermoscopy enhances melanoma detection, but small-diameter melanomas (SDMs) remain diagnostically challenging. Convolutional neural networks (CNNs) may detect subtle patterns beyond human perception. This study evaluates how lesion diameter influences the diagnostic accuracy of dermatologists, with and without CNN support. PATIENTS AND METHODS:This multicenter cross-sectional study included 150 histopathologically verified lesions: 110 small lesions (70 SDM, 40 nevi) and 40 large-diameter melanomas (LDM > 5 mm). Dermatologists evaluated lesions at level: (I) dermoscopy, close-up image, and clinical metadata; (II) plus CNN predictions. Primary outcomes were sensitivity, specificity, and ROC-AUC. RESULTS:The CNN achieved a sensitivity of 62.7% (53.4%-71.2%), specificity of 80.0% (65.2%-89.5%), and ROC-AUC of 0.800 (0.723-0.876). Dermatologists performed higher at 71.8% (62.8%-79.4%), 82.5% (68.1%-91.3%), and 0.853 (0.794-0.912), improving slightly with CNN support to 72.7% (63.7%-80.2%), 85.0% (70.9%-92.9%), and 0.860 (0.796-0.923) (all p > 0.180). Subgroup analyses showed lower CNN performance in SDM, with sensitivity 48.6% (37.3%-60.1%) and ROC-AUC 0.740 (0.644-0.836), compared with LDM at 87.2% (73.3%-94.4%) and 0.904 (0.836-0.973). Dermatologists also reached their highest ROC-AUC in the LDM subgroup (0.925 [0.858-0.991]). Overall, CNN support led to slight, non-significant improvements, most notably in SDM (all p > 0.720). CONCLUSIONS:Lesion size strongly influences melanoma diagnosis. CNN support offers modest, non-significant gains, with highest accuracy achieved by experts using AI assistance.

Importance Convolutional neural networks (CNN) have shown performance equal to trained dermatologists in differentiating benign from malignant skin lesions. To improve clinicians’ management decisions, additional classifications into diagnostic categories might be helpful. Methods A convenience sample of 100 pigmented/non-pigmented skin lesions was used for a cross-sectional two-level reader study including 96 dermatologists (level I: dermoscopy only; level II: clinical close-up images, dermoscopy, and textual information). Dermoscopic images were classified by a binary CNN trained to differentiate melanocytic from non-melanocytic lesions (FotoFinder Systems, Bad Birnbach, Germany). Primary endpoint was the accuracy of the CNN’s classification in comparison with dermatologists reviewing level-II information. Secondary endpoints included dermatologists’ accuracies according to their level of experience and the CNN’s area under the curve (AUC) of receiver operating characteristics (ROC). Results The CNN revealed an accuracy and ROC AUC with corresponding 95% confidence intervals (CI) of 91.0% (83.8% to 95.2%) and 0.981 (0.962 to 1). In level I, dermatologists showed a mean accuracy of 83.7% (82.5% to 84.8%). With level II information, the accuracy improved to 87.8% (86.7% to 88.9%; p < 0.001). When comparing accuracies of CNN and dermatologists in level II, the CNN’s accuracy was higher (91.0% versus 87.8%, P < 0.001). For experts with level II information results were on par with the CNN (91.0% versus 90.4%, p = 0.368). Conclusions The tested CNN accurately differentiated melanocytic from non-melanocytic skin lesions and outperformed dermatologists. The CNN may support clinicians and could be used in an ensemble approach combined with other CNN models.

ZusammenfassungDas Basalzellkarzinom ist der häufigste maligne Tumor der hellhäutigen Bevölkerung mit weiterhin steigender Inzidenz. Ein Update der S2k‐Leitlinie unter Beteiligung aller mit dem Krankheitsbild vertrauten Fachgesellschaften sowie vorangegangener Literaturrecherche ist für die Qualität der Versorgung der betroffenen Patienten von hoher Bedeutung. Neben der Epidemiologie werden Diagnostik und Histologie diskutiert. Die Therapie wird nach einer Risikostratifizierung in topische, systemische und Strahlentherapie gegliedert. Die chirurgische Entfernung bleibt weiterhin in den meisten Fällen die Therapie der ersten Wahl. Durch die Zulassung von Anti‐PD1‐Inhiboren für lokal fortgeschrittene und metastasierte Tumoren ergab sich in der Zweitlinien‐Therapie eine neue Option (nach Hedgehog‐Inhibitoren).

BACKGROUND:The detection of cutaneous metastases (CMs) from various primary tumours represents a diagnostic challenge. OBJECTIVES:Our aim was to evaluate the general characteristics and dermatoscopic features of CMs from different primary tumours. METHODS:Retrospective, multicentre, descriptive, cross-sectional study of biopsy-proven CMs. RESULTS:We included 583 patients (247 females, median age: 64 years, 25%-75% percentiles: 54-74 years) with 632 CMs, of which 52.2% (n = 330) were local, and 26.7% (n = 169) were distant. The most common primary tumours were melanomas (n = 474) and breast cancer (n = 59). Most non-melanoma CMs were non-pigmented (n = 151, 95.6%). Of 169 distant metastases, 54 (32.0%) appeared on the head and neck region. On dermatoscopy, pigmented melanoma metastases were frequently structureless blue (63.6%, n = 201), while amelanotic metastases were typified by linear serpentine vessels and a white structureless pattern. No significant difference was found between amelanotic melanoma metastases and CMs of other primary tumours. CONCLUSIONS:The head and neck area is a common site for distant CMs. Our study confirms that most pigmented melanoma metastasis are structureless blue on dermatoscopy and may mimic blue nevi. Amelanotic metastases are typified by linear serpentine vessels and a white structureless pattern, regardless of the primary tumour.

Basal cell carcinoma is the most common malignant tumor in the fair-skinned population and its incidence continues to rise. An update of the S2k guideline with the participation of all specialist societies familiar with the clinical picture and previous literature research is of great importance for the quality of care for affected patients. In addition to epidemiology, diagnostics and histology are discussed. After risk stratification, therapy is divided into topical, systemic and radiation therapy. Surgical removal remains the treatment of first choice in most cases. The approval of anti-PD1 inhibitors for locally advanced and metastatic tumors has opened up a new option in second-line therapy (after hedgehog inhibitors).

BACKGROUND:As the use of smartphones continues to surge globally, mobile applications (apps) have become a powerful tool for healthcare engagement. Prominent among these are dermatology apps powered by Artificial Intelligence (AI), which provide immediate diagnostic guidance and educational resources for skin diseases, including skin cancer. OBJECTIVE:This article, authored by the EADV AI Task Force, seeks to offer insights and recommendations for the present and future deployment of AI-assisted smartphone applications (apps) and web-based services for skin diseases with emphasis on skin cancer detection. METHODS:An initial position statement was drafted on a comprehensive literature review, which was subsequently refined through two rounds of digital discussions and meticulous feedback by the EADV AI Task Force, ensuring its accuracy, clarity and relevance. RESULTS:Eight key considerations were identified, including risks associated with inaccuracy and improper user education, a decline in professional skills, the influence of non-medical commercial interests, data security, direct and indirect costs, regulatory approval and the necessity of multidisciplinary implementation. Following these considerations, three main recommendations were formulated: (1) to ensure user trust, app developers should prioritize transparency in data quality, accuracy, intended use, privacy and costs; (2) Apps and web-based services should ensure a uniform user experience for diverse groups of patients; (3) European authorities should adopt a rigorous and consistent regulatory framework for dermatology apps to ensure their safety and accuracy for users. CONCLUSIONS:The utilisation of AI-assisted smartphone apps and web-based services in diagnosing and treating skin diseases has the potential to greatly benefit patients in their dermatology journeys. By prioritising innovation, fostering collaboration and implementing effective regulations, we can ensure the successful integration of these apps into clinical practice.

Bislang gibt es in Deutschland kein strukturiertes Programm für die Dermatoskopieausbildung während der Facharztausbildung. Es bleibt der Initiative des einzelnen Assistenzarztes überlassen, ob und in welchem Umfang er sich in der Dermatoskopie weiterbildet, obwohl die Dermatoskopie zu den Kernkompetenzen der dermatologischen Ausbildung und der täglichen Praxis gehört. Ziel der Studie war die Etablierung eines strukturierten Dermatoskopie‐Curriculums während der dermatologischen Facharztausbildung am Universitätsklinikum Augsburg.

Background and objectivesTo date, there is no structured program for dermatoscopy training during residency in Germany. Whether and how much dermatoscopy training is acquired is left to the initiative of each resident, although dermatoscopy is one of the core competencies of dermatological training and daily practice. The aim of the study was to establish a structured dermatoscopy curriculum during residency at the University Hospital Augsburg. Patients and methodsAn online platform with dermatoscopy modules was created, accessible regardless of time and place. Practical skills were acquired under the personal guidance of a dermatoscopy expert. Participants were tested on their level of knowledge before and after completing the modules. Test scores on management decisions and correct dermatoscopic diagnosis were analyzed. ResultsResults of 28 participants showed improvements in management decisions from pre- to posttest (74.0% vs. 89.4%) and in dermatoscopic accuracy (65.0% vs. 85.6%). Pre- vs. posttest differences in test score (7.05/10 vs. 8.94/10 points) and correct diagnosis were significant (p < 0.001). ConclusionsThe dermatoscopy curriculum increases the number of correct management decisions and dermatoscopy diagnoses. This will result in more skin cancers being detected, and fewer benign lesions being excised. The curriculum can be offered to other dermatology training centers and medical professionals.

Dermoscopy aids in melanoma detection; however, agreement on dermoscopic features, including those of high clinical relevance, remains poor. In this study, we attempted to evaluate agreement among experts on exemplar images not only for the presence of melanocytic-specific features but also for spatial localization. This was a cross-sectional, multicenter, observational study. Dermoscopy images exhibiting at least 1 of 31 melanocytic-specific features were submitted by 25 world experts as exemplars. Using a web-based platform that allows for image markup of specific contrast-defined regions (superpixels), 20 expert readers annotated 248 dermoscopic images in collections of 62 images. Each collection was reviewed by five independent readers. A total of 4,507 feature observations were performed. Good-to-excellent agreement was found for 14 of 31 features (45.2%), with eight achieving excellent agreement (Gwet's AC >0.75) and seven of them being melanomaspecific features. These features were peppering/granularity (0.91), shiny white streaks (0.89), typical pigment network (0.83), blotch irregular (0.82), negative network (0.81), irregular globules (0.78), dotted vessels (0.77), and blue-whitish veil (0.76). By utilizing an exemplar dataset, a good-to-excellent agreement was found for 14 features that have previously been shown useful in discriminating nevi from melanoma. All images are public (www.isic-archive.com) and can be used for education, scientific communication, and machine learning experiments.

Importance Studies suggest that convolutional neural networks (CNNs) perform equally to trained dermatologists in skin lesion classification tasks. Despite the approval of the first neural networks for clinical use, prospective studies demonstrating benefits of human with machine cooperation are lacking. Objective To assess whether dermatologists benefit from cooperation with a market-approved CNN in classifying melanocytic lesions. Design, Setting, and Participants In this prospective diagnostic 2-center study, dermatologists performed skin cancer screenings using naked-eye examination and dermoscopy. Dermatologists graded suspect melanocytic lesions by the probability of malignancy (range 0-1, threshold for malignancy ≥0.5) and indicated management decisions (no action, follow-up, excision). Next, dermoscopic images of suspect lesions were assessed by a market-approved CNN, Moleanalyzer Pro (FotoFinder Systems). The CNN malignancy scores (range 0-1, threshold for malignancy ≥0.5) were transferred to dermatologists with the request to re-evaluate lesions and revise initial decisions in consideration of CNN results. Reference diagnoses were based on histopathologic examination in 125 (54.8%) lesions or, in the case of nonexcised lesions, on clinical follow-up data and expert consensus. Data were collected from October 2020 to October 2021. Main Outcomes and Measures Primary outcome measures were diagnostic sensitivity and specificity of dermatologists alone and dermatologists cooperating with the CNN. Accuracy and receiver operator characteristic area under the curve (ROC AUC) were considered as additional measures. Results A total of 22 dermatologists detected 228 suspect melanocytic lesions (190 nevi, 38 melanomas) in 188 patients (mean [range] age, 53.4 [19-91] years; 97 [51.6%] male patients). Diagnostic sensitivity and specificity significantly improved when dermatologists additionally integrated CNN results into decision-making (mean sensitivity from 84.2% [95% CI, 69.6%-92.6%] to 100.0% [95% CI, 90.8%-100.0%]; P = .03; mean specificity from 72.1% [95% CI, 65.3%-78.0%] to 83.7% [95% CI, 77.8%-88.3%]; P < .001; mean accuracy from 74.1% [95% CI, 68.1%-79.4%] to 86.4% [95% CI, 81.3%-90.3%]; P < .001; and mean ROC AUC from 0.895 [95% CI, 0.836-0.954] to 0.968 [95% CI, 0.948-0.988]; P = .005). In addition, the CNN alone achieved a comparable sensitivity, higher specificity, and higher diagnostic accuracy compared with dermatologists alone in classifying melanocytic lesions. Moreover, unnecessary excisions of benign nevi were reduced by 19.2%, from 104 (54.7%) of 190 benign nevi to 84 nevi when dermatologists cooperated with the CNN (P < .001). Most lesions were examined by dermatologists with 2 to 5 years (96, 42.1%) or less than 2 years of experience (78, 34.2%); others (54, 23.7%) were evaluated by dermatologists with more than 5 years of experience. Dermatologists with less dermoscopy experience cooperating with the CNN had the most diagnostic improvement compared with more experienced dermatologists. Conclusions and Relevance In this prospective diagnostic study, these findings suggest that dermatologists may improve their performance when they cooperate with the market-approved CNN and that a broader application of this human with machine approach could be beneficial for dermatologists and patients.