SkiniveMD: for medical doctors vs Model Dermatol - Wiki Usage & Stats

Artificial intelligence analyzes your submitted photographs and instantly searches for relevant medical documents about potential skin conditions. The algorithm provides documents on common skin disorders (e.g., warts, shingles), skin cancers (e.g., melanoma), and other skin rashes (e.g., hives). In the 2022 Stiftung Warentest, a German consumer organization, this application achieved satisfaction ratings only slightly below those of paid teledermatology services. - Please capture photographs of the affected skin area and submit them for analysis. Only the cropped images required for evaluation are transferred; we do not store your personal data. - The algorithm provides links to authoritative medical resources describing the key signs and symptoms of skin conditions and skin cancers (e.g., melanoma). - With the capability to classify 186 distinct skin conditions, the algorithm encompasses common dermatological disorders such as atopic dermatitis, hives, eczema, psoriasis, acne, rosacea, warts, onychomycosis, shingles, melanoma, and nevi. - The use of the algorithm is FREE. However, please keep in mind the following disclaimer: - This app is an image search tool, NOT A DIAGNOSTIC APP. The disease name provided in the contents of the links is not the final diagnosis of skin cancer or a skin disorder. - This app is not a medical device and has not been approved by the FDA. - Although the contents are informative, please CONSULT A DOCTOR before making any medical decisions. We utilize the "Model Dermatology" algorithm, whose performance has been validated and published in multiple peer-reviewed medical journals. Collaborative studies have been conducted with numerous international institutions, including Seoul National University, Yonsei University, Basel University, Stanford University, MSKCC, and Ospedale San Bortolo. Representative publications include: - Assessment of Deep Neural Networks for the Diagnosis of Benign and Malignant Skin Neoplasms in Comparison with Dermatologists: A Retrospective Validation Study. PLOS Medicine, 2020 - Planet-wide Performance of a Skin Disease AI Algorithm Validated in Korea. npj Digital Medicine 2025 - Augmenting the Accuracy of Trainee Doctors in Diagnosing Skin Lesions Suspected of Skin Neoplasms in a Real-World Setting: A Prospective Controlled Before and After Study. PLOS One, 2022 - Performance of a deep neural network in teledermatology: a single center prospective diagnostic study. J Eur Acad Dermatol Venereol. 2020 - Augment Intelligence Dermatology : Deep Neural Networks Empower Medical Professionals in Diagnosing Skin Cancer and Predicting Treatment Options for 134 Skin Disorders. J Invest Dermatol. 2020 - Keratinocytic Skin Cancer Detection on the Face using Region-based Convolutional Neural Network. JAMA Dermatol. 2019 - Classification of the Clinical Images for Benign and Malignant Cutaneous Tumors Using a Deep Learning Algorithm. J Invest Dermatol. 2018 - Evaluation of Artificial Intelligence-assisted Diagnosis of Skin Neoplasms – a single-center, paralleled, unmasked, randomized controlled trial. J Invest Dermatol. 2022 * Disclaimer - Please seek a doctor's advice in addition to using this app and before making any medical decisions. - The diagnosis of skin cancer or skin disorder based solely on clinical images may miss up to 10% of cases. Therefore, this app cannot substitute for standard care (in-person examination). - The algorithm's prediction is not the final diagnosis of skin cancer or skin disorder. It serves only to provide personalized medical information for reference