In the DNN group and control group, these steps are the same process.~Routine exam and capture photographs of skin lesions for all eligible consecutive series patient.~Make a clinical diagnosis (BEFORE-DX)~Make a clinical diagnosis (AFTER-DX)~consult to dermatologist~In the DNN group, after making the BEFORE-DX, physicians use deep neural networks and make an AFTER-DX considering the results of the deep neural networks (Model Dermatology, build 2020).~In the control group, after making the BEFORE-DX, physicians make an AFTER-DX after reviewing the pictures of skin lesions once more.~Ground truth will be based on the biopsy if available, or the consensus diagnosis of the dermatologists.~The investigators will compare the accuracy between the DNN group and control group after 6 consecutive months study.
Background: Deep neural networks (DNN) has been applied to many kinds of skin diseases in experimental settings.~Objective: The objective of this study is to confirm the augmentation of deep neural networks for the diagnosis of skin diseases in non-dermatologist physicians in a real-world setting.~Methods: A total of 40 non-dermatologist physicians in a single tertiary care hospital will be enrolled. They will be randomized to a DNN group and control group. By comparing two groups, the investigators will estimate the effect of using deep neural networks on the diagnosis of skin disease in terms of accuracy.