Artificial intelligence scans the given photograph and instantly assists with your skin problem. AI provides relevant medical information on skin disease (e.g. skin rash, wart, hive) and skin cancer (e.g. melanoma). AI also gives information on the appropriate dermatology clinic. "Model Dermatology" is regulated as a medical device (🞹 CE MDR Class I). The performance of the algorithm has been published in several prestigious medical journals.
◉ Capture skin photographs and submit.
◉ "Model Dermatology" will provide relevant information on dermatology clinics, skin disease, and skin cancer. AI provides personalized links to websites that describe the signs and symptoms of skin disease and skin cancer (e.g. melanoma).
◉ The algorithm can classify 184 skin diseases which include common types of skin disorders (e.g. atopic dermatitis, hive, eczema, psoriasis, acne, rosacea, onychomycosis, melanoma, nevus).
◉ The cropped images and metadata (e.g. itching, pain, onset) are transferred, but we do not store your data.
◉ A total of 104 multi-languages are supported.
🞹 Publication
- 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.
- A total of 10% of cases of skin cancer can be missed if the diagnosis was made using clinical images alone. Therefore, this App can not substitute the role of standard care (in-person examination).
- The prediction of the algorithm is not the final diagnosis of skin cancer or skin disorder. It serves only to provide personalized medical information for reference.
