Model Dermatol - Wiki 14.2.51

IDerma ❘ Freeware ❘
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Artificial intelligence is utilized to analyze provided photographs and aid in finding relevant medical information for skin conditions. The algorithm offers valuable insights on various skin diseases like rashes, warts, and hives.

Here are some specifics about the process:
<ul>
 <li>You can capture skin images and submit them for analysis. Cropped images are used for assessment, without data storage.</li>
 <li>The algorithm directs users to websites detailing signs and symptoms of relevant skin conditions.</li>
 <li>With the capability to categorize images of 186 skin diseases, the algorithm encompasses common skin disorders including atopic dermatitis, eczema, psoriasis, acne, and melanoma.</li>
 <li>This service is FREE of charge and supports 104 languages.</li>
</ul>

However, it is important to consider the following disclaimer:
<ul>
 <li>The algorithm's output is not a definitive diagnosis of skin cancer or other skin disorders; it is intended to provide personalized medical information.</li>
 <li>While this tool can be beneficial, it is recommended to consult with a healthcare professional before making any medical decisions.</li>
</ul>

The application employs the "Model Dermatology" algorithm with proven performance showcased in esteemed medical publications:
<ul>
 <li>"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</li>
 <li>"Performance of a deep neural network in teledermatology: a single center prospective diagnostic study." J Eur Acad Dermatol Venereol. 2020</li>
 <li>"Keratinocytic Skin Cancer Detection on the Face using Region-based Convolutional Neural Network." JAMA Dermatol. 2019</li>
 <li>"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</li>
 <li>"Multiclass Artificial Intelligence in Dermatology: Progress but Still Room for Improvement." J Invest Dermatol. 2020</li>
 <li>"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</li>
 <li>"Interpretation of the Outputs of Deep Learning Model trained with Skin Cancer Dataset." J Invest Dermatol. 2018</li>
 <li>"Automated Dermatological Diagnosis: Hype or Reality?" J Invest Dermatol. 2018</li>
 <li>"Classification of the Clinical Images for Benign and Malignant Cutaneous Tumors Using a Deep Learning Algorithm." J Invest Dermatol. 2018</li>
 <li>"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</li>
 <li>"Evaluation of Artificial Intelligence-assisted Diagnosis of Skin Neoplasms – a single-center, paralleled, unmasked, randomized controlled trial." J Invest Dermatol. 2022</li>
</ul>

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