Model Dermatol – 皮膚病，皮膚癌 簡介
人工智能掃描提供的照片並立即協助您解決皮膚問題。該人工智能提供有關皮膚病（例如皮疹、疣、蕁麻疹）和皮膚癌（例如黑色素瘤）的相關醫學資訊，還提供有關適當皮膚科診所的資訊。 "Model Dermatology" 經監管為醫療器械（🞹 CE-MDR I 類），且該算法的表現已在多個知名醫學期刊上發表。
◉ 拍攝皮膚照片並提交。
◉ "Model Dermatology" 將提供有關皮膚科診所、皮膚病和皮膚癌的相關資訊。該人工智能提供個性化鏈接，以描述皮膚病和皮膚癌（例如黑色素瘤）的體徵和症狀的網站。
◉ 該算法可對184種皮膚病進行分類，包括常見的皮膚病類型（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）。
◉ 提交的圖像和元數據（例如瘙癢、疼痛、發作）會被傳輸，但我們不會存儲您的數據。
◉ 該算法使用免費，並支持104種多語言。
🞹 出版
- 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
- 除了使用這個應用程序和做出任何醫療決定之前，請尋求醫生的建議。
- 如果僅使用臨床圖像進行診斷，則總共有 10% 的皮膚癌病例可能被漏診。因此，此應用程序不能替代標準護理（親自檢查）的作用。
- 算法的預測不是皮膚癌或皮膚病的最終診斷。它僅用於提供個性化的醫療信息以供參考。
◉ 拍攝皮膚照片並提交。
◉ "Model Dermatology" 將提供有關皮膚科診所、皮膚病和皮膚癌的相關資訊。該人工智能提供個性化鏈接，以描述皮膚病和皮膚癌（例如黑色素瘤）的體徵和症狀的網站。
◉ 該算法可對184種皮膚病進行分類，包括常見的皮膚病類型（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）。
◉ 提交的圖像和元數據（例如瘙癢、疼痛、發作）會被傳輸，但我們不會存儲您的數據。
◉ 該算法使用免費，並支持104種多語言。
🞹 出版
- 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
- 除了使用這個應用程序和做出任何醫療決定之前，請尋求醫生的建議。
- 如果僅使用臨床圖像進行診斷，則總共有 10% 的皮膚癌病例可能被漏診。因此，此應用程序不能替代標準護理（親自檢查）的作用。
- 算法的預測不是皮膚癌或皮膚病的最終診斷。它僅用於提供個性化的醫療信息以供參考。
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