Model Dermatol - Wiki

人工智慧可以分析提供的照片並立即幫助查找有關皮膚問題的信息。該算法提供有關皮膚疾病（如疣、帶狀皰疹）、皮膚癌（如黑色素瘤）和其他皮膚疹（如蕁麻疹）的相關醫療信息。在2022年德國消費者組織Stiftung Warentest的評測中，該應用的滿意度評分僅略低於付費遠程醫學皮膚科服務。
- 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。
- 人工智能提供了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 種皮膚疾病的圖像進行分類，包括常見的皮膚疾病類型（例如特應性皮炎、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。
- 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