Model Dermatol – 皮膚病，皮膚癌

人工智能搜索皮膚病信息

人工智能會掃描所提供的照片，並立即幫助識別您的皮膚問題。AI 還提供有關皮膚病（例如皮疹、疣、蕁麻疹）和皮膚癌（例如黑色素瘤）的相關醫學信息。

◉ 拍攝皮膚照片並提交。裁剪後的圖像會被傳輸，但我們不會存儲您的數據。

◉ 人工智能提供描述皮膚病和皮膚癌（例如黑色素瘤）相關體徵和症狀的網站鏈接。

◉ 該算法可以對186種皮膚病的圖像進行分類，包括常見的皮膚病類型（如特應性皮炎、蕁麻疹、濕疹、牛皮癬、痤瘡、酒渣鼻、甲癬、黑色素瘤、痣）。

◉ 算法使用免費，共支持104種語言。

🞹 出版物

我們使用“Model Dermatology”算法。分類器的性能已發表在幾家著名的醫學期刊上。許多合作研究已與多家國際醫院進行，包括 Seoul National University, Ulsan University, Yonsei University, Hallym University, Inje University, Stanford, MSKCC 和 Ospedale San Bortolo。

- 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

🞹 免責聲明

- 除了使用此應用程序之外，在做出任何醫療決定之前，請尋求醫生的建議。

- 僅根據臨床圖像診斷皮膚癌或皮膚病可能會漏掉多達 10% 的病例。因此，此應用程序不能替代標準護理（親自檢查）。

- 該算法的預測不是皮膚癌或皮膚病的最終診斷。僅供參考，提供個性化醫療信息