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.

Background: Mobile health (mHealth) consumer applications (apps) have been integrated with deep learning for skin cancer risk assessments. However, prospective validation of these apps is lacking. Objectives: To identify the diagnostic accuracy of an app integrated with a convolutional neural network for the detection of premalignant and malignant skin lesions. Methods: We performed a prospective multicenter diagnostic accuracy study of a CE-marked mHealth app from January 1 until August 31, 2020, among adult patients with at least one suspicious skin lesion. Skin lesions were assessed by the app on an iOS or Android device after clinical diagnosis and before obtaining histopathology. The app outcome was compared to the histopathological diagnosis, or if not available, the clinical diagnosis by a dermatologist. The primary outcome was the sensitivity and specificity of the app to detect premalignant and malignant skin lesions. Subgroup analyses were conducted for different smartphone types, the lesion’s origin, indication for dermatological consultation, and lesion location. Results: In total, 785 lesions, including 418 suspicious and 367 benign control lesions, among 372 patients (50.8% women) with a median age of 71 years were included. The app performed at an overall 86.9% (95% CI 82.3–90.7) sensitivity and 70.4% (95% CI 66.2–74.3) specificity. The sensitivity was significantly higher on the iOS device compared to the Android device (91.0 vs. 83.0%; p = 0.02). Specificity calculated on benign control lesions was significantly higher than suspicious skin lesions (80.1 vs. 45.5%; p < 0.001). Sensitivity was higher in skin fold areas compared to smooth skin areas (92.9 vs. 84.2%; p = 0.01), while the specificity was higher for lesions in smooth skin areas (72.0 vs. 56.6%; p = 0.02). Conclusion: The diagnostic accuracy of the mHealth app is far from perfect, but is potentially promising to empower patients to self-assess skin lesions before consulting a health care professional. An additional prospective validation study, particularly for suspicious pigmented skin lesions, is warranted. Furthermore, studies investigating mHealth implementation in the lay population are needed to demonstrate the impact on health care systems.

Background Mobile health (mHealth) applications (apps) integrated with artificial Intelligence (AI) for skin cancer screening are increasingly reimbursed by health insurers. However, an in‐depth exploration of the general public’s views towards these apps is lacking. Objectives To explore the perceived barriers and facilitators towards mHealth apps for skin cancer screening among the Dutch general population. Methods A qualitative study consisting of four focus groups with 27 participants was conducted. A two‐stage purposive sampling was used to include information‐rich participants with varying experience with mHealth from the Dutch general population. A topic guide was used to structure the sessions. All focus groups were transcribed verbatim and analyzed in a thematic content analysis by two researchers using several coding phases, resulting in an overview of themes and subthemes, categorized as (sub‐)barriers and (sub‐)facilitators. Results Main barriers to using mHealth apps included a perceived lack of value, perception of untrustworthiness, the preference for a doctor, privacy concerns, a complex user‐interface, and high costs. The main factors facilitating the use of mHealth among the general population were a high perceived value, a transparent and trustworthy identity of app developers, endorsement by healthcare providers and government regulating bodies, and ease and low costs of use. Conclusions To increase successful adoption in skin cancer screening apps, developers should create a transparent identity and build trustworthy apps. Collaboration between app developers, GPs and dermatologists is advocated to improve mHealth integration in skin cancer care. Special attention should be given to the development of low‐cost, privacy‐friendly, easy‐to‐use apps.

Recent studies show promising potential for artificial intelligence (AI) to assist healthcare providers (HCPs) in skin cancer care. The aim of this study is to explore the views of dermatologists and general practitioners (GPs) regarding the successful implementation of AI when assisting HCPs in skin cancer care. We performed a qualitative focus group study, consisting of six focus groups with 16 dermatologists and 17 GPs, varying in prior knowledge and experience with AI, gender, and age. An in-depth inductive thematic content analysis was deployed. Perceived benefits, barriers, and preconditions were identified as main themes. Dermatologists and GPs perceive substantial benefits of AI, particularly an improved health outcome and care pathway between primary and secondary care. Doubts about accuracy, risk of health inequalities, and fear of replacement were among the most stressed barriers. Essential preconditions included adequate algorithm content, sufficient usability, and accessibility of AI. In conclusion, dermatologists and GPs perceive significant benefits from implementing AI in skin cancer care. However, to successfully implement AI, key barriers need to be addressed. Efforts should focus on ensuring algorithm transparency, validation, accessibility for all skin types, and adequate regulation of algorithms. Simultaneously, improving knowledge about AI could reduce the fear of replacement.

Background The COVID-19 pandemic reduced the number of skin cancer diagnoses, potentially causing a progression to unfavorable tumor stages. Objective To identify the impact of delayed diagnostics on primary invasive melanoma and cutaneous squamous cell carcinoma (cSCC) by comparing tumor (pT) stage, Breslow thickness and invasion depth from pre to post the first and second lockdown periods. Methods In this population-based cohort study, histopathology reports registered between 01-01-2018 and 22-7-2021 were obtained from the nationwide histopathology registry in the Netherlands. The Breslow thickness of melanomas, invasion depth of cSCCs, and pT-stage for both tumor types were compared across five time periods: (i) pre-COVID, (ii) first lockdown, (iii) between first and second lockdown, (iv) second lockdown, and (v) after second lockdown. Breslow thickness was compared using an independent t-test. pT-stage groups were compared using a χ²-test. Outcomes were corrected for multiple testing using the false discovery rate. Results In total, 20,434 primary invasive melanomas and 68,832 cSCCs were included in this study. The mean primary melanoma Breslow thickness of pre-pandemic era (i) and the following time periods (ii-v) showed no significant difference. A small shift was found towards unfavorable pT-stages during the first lockdown compared to the pre-COVID period: pT1 52.3% vs. 58.6%; pT2 18.9% vs. 17.8%; pT3 13.2% vs. 11.0%; pT4 9.1% vs. 7.3% (p=0.001). No relevant changes were seen in subsequent periods. No significant change in pT-stage distribution was observed between the pre-COVID (i) and COVID-affected periods (ii-v) for cSCCs. Conclusion Until now, the diagnostic delay by COVID-19 did not result in relatively more unfavorable primary tumor characteristics of melanoma or cSCC. Follow-up studies in the coming years are needed to identify a potential impact on staging distribution and survival in the long term.