{"id":63,"date":"2021-01-03T07:06:29","date_gmt":"2021-01-03T07:06:29","guid":{"rendered":"http:\/\/ai.skindx.net\/?page_id=63"},"modified":"2023-02-19T04:16:10","modified_gmt":"2023-02-19T04:16:10","slug":"algorithm","status":"publish","type":"page","link":"https:\/\/ai.skindx.net\/index.php\/algorithm","title":{"rendered":"Algorithm"},"content":{"rendered":"\r\n
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This person is not a real man. The image was generated using AI technology (GAN).<\/figcaption><\/figure>\r\n

Model Dermatology (https:\/\/app.skindx.net<\/a>) has been validated in several academic studies involving several prestigious university hospitals worldwide (i.e. Researchers from Korea, the United States, Chile, and Greece participated in the validation studies). The algorithm has been trained using delicately balanced datasets.<\/p>\r\n

The performance was comparable with that of dermatologists in the experimental settings when the diagnosis was made solely with clinical photographs. For diagnosing suspected skin lesions, the performance of our multiclass algorithm was comparable with that of dermatology residents in the real-world setting. We demonstrated augmented intelligence in the prospective randomized clinical trial.\u00a0<\/p>\r\n\r\n\r\n\r\n

\"\" The performance was comparable with that of dermatologists in the experimental setting.<\/span><\/span><\/p>\r\n

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Test = SNU dataset, 133 disease classes, 2201 images; Scientific Report, 2022<\/figcaption><\/figure>\r\n

\"\" The algorithm could augment the performance of physicians in the real-world setting.<\/span><\/span><\/p>\r\n

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Randomized Controlled Trial; J Invest Dermatol. 2022<\/figcaption><\/figure>\r\n

\"\"The algorithm could triage suspected skin lesions at the level of general physicians in the cohort validation using patient-captured images.<\/span><\/span><\/p>\r\n

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RD dataset consists of 1,282 consecutive images of an internet melanoma community (Reddit melanoma); Scientific Report, 2022<\/figcaption><\/figure>\r\n

\"\"Clinical Study<\/span>\u00a0<\/p>\r\n\r\n\r\n\r\n

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  1. 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<\/a><\/li>\r\n
  2. Performance of a deep neural network in teledermatology: a single\u2010center prospective diagnostic study. J Eur Acad Dermatol Venereol. 2020<\/a><\/li>\r\n
  3. Keratinocytic Skin Cancer Detection on the Face using Region-based Convolutional Neural Network. JAMA Dermatol. 2019<\/a><\/li>\r\n
  4. 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<\/a><\/li>\r\n
  5. Multiclass Artificial Intelligence in Dermatology: Progress but Still Room for Improvement. J Invest Dermatol. 2020<\/a><\/li>\r\n
  6. 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<\/a><\/li>\r\n
  7. Interpretation of the Outputs of Deep Learning Model trained with Skin Cancer Dataset. J Invest Dermatol. 2018<\/a><\/li>\r\n
  8. Automated Dermatological Diagnosis: Hype or Reality? J Invest Dermatol. 2018<\/a><\/li>\r\n
  9. Classification of the Clinical Images for Benign and Malignant Cutaneous Tumors Using a Deep Learning Algorithm. J Invest Dermatol. 2018<\/a><\/li>\r\n
  10. 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<\/a><\/li>\r\n
  11. Evaluation of Artificial Intelligence-assisted Diagnosis of Skin Neoplasms \u2013 a single-center, paralleled, unmasked, randomized controlled trial. J Invest Dermatol. 2022<\/a><\/li>\r\n
  12. The Degradation of Performance of a State-of-the-art Skin Image Classifier When Applied to Patient-driven Internet Search. Scientific Report 2022<\/a><\/li>\r\n<\/ol>\r\n\r\n\r\n\r\n

    \"\"Commentary<\/span><\/p>\r\n

      \r\n
    1. Toward Augmented Intelligence: The First Prospective, Randomized Clinical Trial Assessing Clinician and Artificial Intelligence Collaboration in Dermatology \u2013 J Invest Dermatol. 2022<\/a>\u00a0<\/li>\r\n
    2. Automated Classification of Skin Lesions: From Pixels to Practice \u2013 J. Invest Dermatol. 2018<\/a><\/li>\r\n
    3. Problems and Potentials of Automated Object Detection for Skin Cancer Recognition \u2013 JAMA Dermatol. 2020<\/a><\/li>\r\n<\/ol>\r\n

      \"\"Magazine<\/span><\/p>\r\n\r\n\r\n\r\n