Model Dermatology has been validated in several academic studies involving several prestigious university hospitals worldwide (i.e. Researchers from Korea, the United States, Chile, and Greece participated in the validation studies). The algorithm has been trained using delicately balanced datasets.
The performance was comparable with that of dermatologists in the experimental settings when the diagnosis was made solely with clinical photographs. For diagnosing suspected skin lesions, the performance of our multiclass algorithm was comparable with that of dermatology residents in the real-world setting. We demonstrated augmented intelligence in the first prospective randomized clinical trial.
- 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
- Performance of a deep neural network in teledermatology: a single‐center prospective diagnostic study. J Eur Acad Dermatol Venereol. 2020
- Keratinocytic Skin Cancer Detection on the Face using Region-based Convolutional Neural Network. JAMA Dermatol. 2019
- 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
- Multiclass Artificial Intelligence in Dermatology: Progress but Still Room for Improvement. J Invest Dermatol. 2020
- 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
- Interpretation of the Outputs of Deep Learning Model trained with Skin Cancer Dataset. J Invest Dermatol. 2018
- Automated Dermatological Diagnosis: Hype or Reality? J Invest Dermatol. 2018
- Classification of the Clinical Images for Benign and Malignant Cutaneous Tumors Using a Deep Learning Algorithm. J Invest Dermatol. 2018
- 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
- Evaluation of Artificial Intelligence-assisted Diagnosis of Skin Neoplasms – a single-center, paralleled, unmasked, randomized controlled trial. J Invest Dermatol. 2022
- Toward Augmented Intelligence: The First Prospective, Randomized Clinical Trial Assessing Clinician and Artificial Intelligence Collaboration in Dermatology – J Invest Dermatol. 2022
- Automated Classification of Skin Lesions: From Pixels to Practice – J. Invest Dermatol. 2018
- Problems and Potentials of Automated Object Detection for Skin Cancer Recognition – JAMA Dermatol. 2020
- AI Beats Dermatologists in Diagnosing Nail Fungus (IEEE Spectrum, Feb. 2018)
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….
- New artificial intelligence system can empower medical professionals in diagnosing skin diseases (EurekAlert, March 2020)
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…..
It is important 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, Konstantinos Liopyris, Roxana Daneshjou, and Allan Halpern who performed the external validation of the algorithm.