Artificial intelligence can analyze the provided photograph and instantly help to find medical information for your skin problem. The algorithm offers relevant medical information on skin diseases such as skin rash, wart, and hive.
Here are some details about the process:
- You can capture skin photographs and submit them for analysis. The cropped images will be transferred, but we will not store your data.
- The algorithm provides links to websites that describe the relevant signs and symptoms of skin diseases.
- With the ability to classify images of 186 skin diseases, the algorithm covers common types of skin disorders such as atopic dermatitis, hive, eczema, psoriasis, acne, rosacea, onychomycosis, melanoma, and nevus.
- The use of the algorithm is FREE, and it supports a total of 104 languages.
However, please keep in mind the following disclaimer:
- The algorithm's prediction is not the final diagnosis of skin cancer or skin disorder although it is meant to provide personalized medical information.
- While this app is useful, please consult a doctor before making any medical decisions.
We utilize the "Model Dermatology" algorithm. The classifier's performance has been published in several prestigious medical journals.
- 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
Here are some details about the process:
- You can capture skin photographs and submit them for analysis. The cropped images will be transferred, but we will not store your data.
- The algorithm provides links to websites that describe the relevant signs and symptoms of skin diseases.
- With the ability to classify images of 186 skin diseases, the algorithm covers common types of skin disorders such as atopic dermatitis, hive, eczema, psoriasis, acne, rosacea, onychomycosis, melanoma, and nevus.
- The use of the algorithm is FREE, and it supports a total of 104 languages.
However, please keep in mind the following disclaimer:
- The algorithm's prediction is not the final diagnosis of skin cancer or skin disorder although it is meant to provide personalized medical information.
- While this app is useful, please consult a doctor before making any medical decisions.
We utilize the "Model Dermatology" algorithm. The classifier's performance has been published in several prestigious medical journals.
- 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
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