Model Dermatol – Skin Disease APP
- 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.
- This application functions solely as an image search tool and is NOT a diagnostic platform. Disease names provided via linked content do not constitute a confirmed diagnosis of skin cancer or other dermatological conditions. While the information provided is medically informative, it is essential to CONSULT A PHYSICIAN before making any healthcare decisions.
- The use of this algorithm is completely FREE.
However, please keep in mind the following disclaimer:
- This app is an image search tool, NOT A DIAGNOSTIC APP. The disease names provided in the linked content are not final diagnoses of skin cancer or skin disorders.
- This app is not a medical device and has not been approved by the FDA.
- Although the content is 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


