Model Dermatol – Skin Disease

Iderma international Inc.

About this app

Artificial intelligence can analyze the provided photograph and instantly help find information about your skin problem. The algorithm provides relevant medical information on skin diseases (e.g., warts, shingles), skin cancer (e.g., melanoma), and other skin rashes (e.g., hives). In the 2022 Stiftung Warentest, a German consumer organization, this app received satisfaction ratings only slightly lower than paid telemedicine dermatology services.

- 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 diseases 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, 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 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. 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

Ratings and reviews

I tried taking the pictures with and without flash, and in a well lit area. The pictures would look great until I added them, then they would become dark and blurry. The few pictures I could use were suggested to possibly be several different things, which I could have just googled and found out the same. So, this app didn't give me any information I didn't already have.

Bad app, doesn't identify anything, even acne from top google results edit: after dev replied I downloaded again and tried another google image, their AI idnetified basic comedonal acne as a rash, still a 1 star.

It's a basic looking app with. Slightly confusing options , but it has seemed to generate fair results