Model Dermatol - Wiki

人工智能可以分析提供的照片，并立即帮助找到有关皮肤问题的信息。该算法提供有关皮肤疾病（如疣、带状疱疹）、皮肤癌（如黑色素瘤）和其他皮疹（如荨麻疹）的相关医学信息。在2022年，德国消费者组织Stiftung Warentest对该应用程序的满意度评分仅略低于付费远程皮肤病诊疗服务。
- 拍摄皮肤照片并提交。裁剪后的图像会被传输，但我们不会存储您的数据。
- 人工智能提供了描述皮肤病和皮肤癌（例如黑色素瘤）相关体征和症状的网站链接。
- 该算法可以对 186 种皮肤疾病的图像进行分类，包括常见的皮肤疾病类型（例如特应性皮炎、荨麻疹、湿疹、牛皮癣、痤疮、酒渣鼻、甲癣、黑色素瘤、痣）。
- 算法使用免费，共支持104种语言。
* 出版物
我们使用“Model Dermatology”算法。分类器的性能已发表在几家著名的医学期刊上。
- 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
* 免责声明
- 除了使用此应用程序之外，在做出任何医疗决定之前，请寻求医生的建议。
- 仅根据临床图像诊断皮肤癌或皮肤病可能会漏掉多达 10% 的病例。因此，此应用程序不能替代标准护理（亲自检查）。
- 该算法的预测不是皮肤癌或皮肤病的最终诊断。仅供参考，提供个性化医疗信息