Skin diseases

SkinDiseasesAI aims to detect all known skin diseases. With this module, our goal is to help users to follow up their skin conditions themselves and consult a doctor if necessary.

The skin is the largest organ of the body, as a consequence, there are many skin problems. Some may look good but instead be cancerous, some might appear very ugly and scare people and yet be benign. With this module, we hope to help users not to worry in case of a benign lesion and warn them in case of doubt of malignancy. Our algorithm has learned from more than 160 thousand cases and can detect up to 900 diseases, among other skin conditions. Its purpose is to detect all skin problems in the near future and it works for every skin type.

Large and small companies are currently working with several tools using artificial intelligence to detect skin problems, particularly skin cancer. Our value lies precisely in developing proprietary algorithms based on our know-how, which guarantees maximum understanding and comprehension of them, and not in using pre-designed algorithms whose comprehension is kept in a black box.

Our researchers, most of them PhDs in Applied Mathematics, put all their knowledge in mathematics and informatics into the process of building SkinDiseasesAI. Moreover, although Dermatology is a field outside our domain, we have involved Dermatologists from several countries around the world (Vietnam, France, USA, Cuba, Italy, etc.) in the process of annotations and verification of the disease images.

In order to use our SkinDiseasesAI, users just have to upload a clear image containing the lesion they want to consult. Our machine will then give them a list of conditions with a corresponding number for each, representing the confidence level of the prediction for that disease.

Once it’s uploaded, it takes less than one second to return the result,  given by the aforementioned list of possible diseases. The system will link each disease to our Skin Encyclopedia that will help users understand and learn about the predicted diseases.