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Further reading

Introducción

This artificial intelligence has been developed in collaboration with dozens of dermatologists, specialized in respective pathologies. To do this, the team that has developed the technology has collected hundreds of thousands of images from different sources, containing both clinical and dermoscopic images, of different sizes, perspectives and lighting conditions.

The technology consists of the use of Convolutional Neural Networks (CNN) that process images and learn from the consensus of the doctors who participate in their creation.

Below we list a series of publications in scientific journals related to the operation and performance of the algorithm. In these publications you will find descriptions of the method used to create artificial intelligence, as well as the results, and the names of the doctors who have participated in its creation and validation.

Automatic International Hidradenitis Suppurativa Severity Score System (AIHS4): A Novel Tool to Assess the Severity of Hidradenitis Suppurativa Using Artificial Intelligence

Dermatology Image Quality Assessment (DIQA): Artificial intelligence to ensure the clinical utility of images for remote consultations and clinical trials

Automatic SCOring of Atopic Dermatitis Using Deep Learning: A Pilot Study

Diego Herrera (Almirall) & Taig MacCarthy (Legit.Health) Digitalising clinical endpoints with AI