This person is not a real man. The image was generated using AI technology (GAN).

Model Dermatology ( has been validated in multiple academic studies conducted by prestigious university hospitals worldwide, including researchers from Korea, the United States, Chile, Greece, and Italy. The algorithm was trained using carefully balanced datasets.

In experimental settings where the diagnosis was made solely with clinical photographs, the algorithm’s performance was comparable to that of dermatologists. For diagnosing suspected skin lesions, our multiclass algorithm’s performance was similar to that of dermatology residents in real-world settings. We demonstrated the efficacy of augmented intelligence in a prospective randomized clinical trial.

However, in a melanoma study conducted in Italy, the algorithm's predictions did not align with those of dermatologists. Additionally, the algorithm that relies solely on photographs for diagnosis failed to match the accuracy of diagnoses made by actual dermatologists who incorporate various information.

The performance was comparable with that of dermatologists in the experimental setting.

Test = SNU dataset, 133 disease classes, 2201 images; Scientific Report, 2022

The algorithm could augment the performance of physicians in the real-world setting.

Randomized Controlled Trial; J Invest Dermatol. 2022

The algorithm could triage suspected skin lesions at the level of general physicians in the cohort validation using patient-captured images.

RD dataset consists of 1,282 consecutive images of an internet melanoma community (Reddit melanoma); Scientific Report, 2022

Clinical Study 

  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 single‐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
  10. 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
  11. Evaluation of Artificial Intelligence-assisted Diagnosis of Skin Neoplasms – a single-center, paralleled, unmasked, randomized controlled trial. J Invest Dermatol. 2022
  12. The Degradation of Performance of a State-of-the-art Skin Image Classifier When Applied to Patient-driven Internet Search. Scientific Report 2022
  13. Generation of a melanoma and nevus data set from unstandardized clinical photographs on the internet. JAMA dermatology 2023
  14. Melanoma detection: Evaluating the classification performance of a deep convolutional neural network and dermatologist assessment via a mobile app in an Italian real-world setting. J Eur Acad Dermatol Venereol. 2024


  1. Toward Augmented Intelligence: The First Prospective, Randomized Clinical Trial Assessing Clinician and Artificial Intelligence Collaboration in Dermatology – J Invest Dermatol. 2022 
  2. Automated Classification of Skin Lesions: From Pixels to Practice – J. Invest Dermatol. 2018
  3. Problems and Potentials of Automated Object Detection for Skin Cancer Recognition – JAMA Dermatol. 2020


The latest successful demonstration of AI’s capabilities in the medical field relied heavily upon a team of South Korean researchers putting together a huge dataset of almost 50,000 images of toenails and fingernails. That large amount of data used to train the deep neural networks on recognizing cases of onychomycosis—a common fungal infection that can make nails discolored and brittle—provided the crucial edge that enabled deep learning to outperform medical experts….

Researchers in Korea have developed a deep learning-based artificial intelligence (AI) algorithm that can accurately classify cutaneous skin disorders, predict malignancy, suggest primary treatment options, and serve as an ancillary tool to enhance the diagnostic accuracy of clinicians…..

Short summary of the randomized controlled trial.

Stiftung Warentest was conducting an investigation into “skin screening apps”.


It is essential that AI ​​does well in the real-world setting. Furthermore, AI should be able to change the decision of doctors or patients. However, since the gap between the results of prospective and retrospective research is quite high, it is necessary to narrow down the scope of the problem, and we need to make a lot of effort to improve the data….


Model Dermatology has been developed with the contribution of many academic researchers. Seung Seog Han is leading the project. Sung Eun Chang, Jung-Im Na, Seong Hwan Kim, Myoung Shin Kim, Gyeong Hun Park, Soo Ick Cho, Woohyung Lim, Ik Jun Moon, Young jae Kim, and Ilwoo Park have contributed to the development of the algorithm since 2016. We are also grateful to Cristian Navarrete-Dechent‬, Camila Muñoz-López, Konstantinos Liopyris, Roxana Daneshjou, Allan Halpern, Fortunato Cassalia, and Luigi Naldi who performed the external validation of the algorithm. Mauricio Caceres and Wail Atra have contributed to the internalization of the app.