Model Dermatol – Skin Disease

Artificial intelligence scans the provided photograph and instantly assists with identifying your skin problem. The AI provides relevant medical information on skin diseases (e.g., skin rash, wart, hive) and skin cancer (e.g., melanoma).

◉ Capture skin photographs and submit them. The cropped images are transferred, but we do not store your data.
◉ The AI provides links to websites that describe the relevant signs and symptoms of skin disease and skin cancer (e.g., melanoma).
◉ The algorithm can classify images of 186 skin diseases, including common types of skin disorders (e.g., atopic dermatitis, hive, eczema, psoriasis, acne, rosacea, onychomycosis, melanoma, nevus).
◉ The use of the algorithm is free and a total of 104 languages are supported.

🞹 Publication
We utilize the "Model Dermatology" algorithm. The classifier's performance has been published in several prestigious medical journals.
- 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