Model Dermatology is freely available. However, the submitted images and metadata are transferred and stored for research purposes.

Our algorithms have been validated in several academic studies. The performance was comparable with that of dermatologists in the experimental settings when the diagnosis was made solely with clinical photographs. The algorithm and apps are maintained by Han Seung Seog from I Dermatology Clinic.

  • Studies related with the algorithm
  1. 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
  2. Performance of a deep neural network in teledermatology: a singleā€center prospective diagnostic study. J Eur Acad Dermatol Venereol. 2020
  3. Keratinocytic Skin Cancer Detection on the Face using Region-based Convolutional Neural Network. JAMA Dermatol. 2019
  4. 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
  5. Multiclass Artificial Intelligence in Dermatology: Progress but Still Room for Improvement. J Invest Dermatol. 2020
  6. 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
  7. Interpretation of the Outputs of Deep Learning Model trained with Skin Cancer Dataset. J Invest Dermatol. 2018
  8. Automated Dermatological Diagnosis: Hype or Reality? J Invest Dermatol. 2018
  9. Classification of the Clinical Images for Benign and Malignant Cutaneous Tumors Using a Deep Learning Algorithm. J Invest Dermatol. 2018
  • Preprint
  1. Augmenting the Accuracy of Trainee Doctors in Diagnosing Skin Neoplasms in a Real-World Setting: A Prospective Before and After Study. 2020 (preprint;