Model Dermatol - Wiki

Artificial intelligence can analyze the provided photograph and instantly help to find diagnoses for your skin problem. The algorithm identifies skin diseases (e.g. wart, shingles), skin cancer (e.g. melanoma), and other skin rashes (e.g. hive) with photos and provides relevant medical information. - Please capture skin photographs and submit them for analysis. The cropped images will be transferred, but we will not store your data. - The algorithm provides links to websites that describe the relevant signs and symptoms of skin disease and skin cancer (e.g. melanoma). - With the ability to classify images of 186 skin diseases, the algorithm covers common types of skin disorders such as atopic dermatitis, hive, eczema, psoriasis, acne, rosacea, wart, onychomycosis, shingles, melanoma, and nevus. - 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. - Although the contents are informative, please CONSULT A DOCTOR before making any medical decisions. We utilize the "Model Dermatology" algorithm. The classifier's performance has been published in several prestigious medical journals. Numerous collaborative studies have been conducted with various hospitals internationally, including Seoul National University, Ulsan University, Yonsei University, Hallym University, Inje University, Stanford, MSKCC, and Ospedale San Bortolo. - 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 - Performance of a deep neural network in teledermatology: a single center prospective diagnostic study. J Eur Acad Dermatol Venereol. 2020 - Keratinocytic Skin Cancer Detection on the Face using Region-based Convolutional Neural Network. JAMA Dermatol. 2019 - Seems to be low, but is it really poor? : Need for Cohort and Comparative studies to Clarify Performance of Deep Neural Networks. J Invest Dermatol. 2020 - Multiclass Artificial Intelligence in Dermatology: Progress but Still Room for Improvement. J Invest Dermatol. 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 - Interpretation of the Outputs of Deep Learning Model trained with Skin Cancer Dataset. J Invest Dermatol. 2018 - Automated Dermatological Diagnosis: Hype or Reality? J Invest Dermatol. 2018 - Classification of the Clinical Images for Benign and Malignant Cutaneous Tumors Using a Deep Learning Algorithm. J Invest Dermatol. 2018 - 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 - 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