Model Dermatol – Skin Disease APP
인공지능 알고리즘은 주어진 사진을 분석해서, 검색엔진보다 훨씬 더 정확한 맞춤형 의학 정보 (피부암, 피부질환) 를 제공합니다. 알고리즘의 성능은 국내외 여러 대학병원과의 임상연구를 통해 검증되었고 연구결과는 해외 피부과 최상위 학술지 (인용수 기준 상위 5%) 에 여러차례 게재되었습니다. 다수의 임상연구가 한국, 미국, 칠레, 그리스의 의료기관에서 이루어졌습니다.
* 연구 논문 리스트 *
- 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