×
与传统的互联网搜索相比，人工智能提供更准确的答案。 该算法根据图像结果提供相关疾病信息。它在维基网站上提供最相关的皮肤病信息，以及进一步互联网搜索的链接。该算法在网上免费提供，支持104种语言。（https://www.modelderm.com）该算法可以通过应用市场下载，也可以使用以下 APK 链接直接安装： 下载
皮肤科模型 已在全球多个著名大学医院进行的多个学术研究中得到验证，包括来自韩国、美国、智利和希腊的研究人员。该算法是使用谨慎平衡的数据集进行训练的。
在仅使用临床照片进行诊断的实验设置中，该算法的性能与皮肤科医生相当。对于诊断可疑皮肤病变，我们的多类别算法在实际环境中的性能与皮肤科住院医师相似。我们在一项前瞻性随机临床试验中展示了增强智能的功效。
在实验环境中，该性能与皮肤科医生相当。
Test = SNU dataset, 133 disease classes, 2201 images; Scientific Report, 2022 算法可以增强医生在实际环境中的表现。
Randomized Controlled Trial; J Invest Dermatol. 2022 该算法可以使用患者捕捉的图像，在队列验证中对可疑皮肤病变进行初步分类，达到一般医生的水平。
RD dataset consists of 1,282 consecutive images of an internet melanoma community (Reddit melanoma); Scientific Report, 2022 Clinical Study
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 The Degradation of Performance of a State-of-the-art Skin Image Classifier When Applied to Patient-driven Internet Search. Scientific Report 2022Commentary
Toward Augmented Intelligence: The First Prospective, Randomized Clinical Trial Assessing Clinician and Artificial Intelligence Collaboration in Dermatology – J Invest Dermatol. 2022 Automated Classification of Skin Lesions: From Pixels to Practice – J. Invest Dermatol. 2018 Problems and Potentials of Automated Object Detection for Skin Cancer Recognition – JAMA Dermatol. 2020Magazine
AI Beats Dermatologists in Diagnosing Nail Fungus (IEEE Spectrum, Feb. 2018)在医学领域，人工智能最新成功的演示主要依赖于一组韩国研究人员组建了一个庞大的数据集，其中包含近50,000张脚趾和指甲的图像。用于训练深度神经网络以识别甲癣症病例的大量数据——甲癣症是一种常见的真菌感染，可以使指甲变色和变脆——为深度学习提供了关键优势，使其在性能上超过了医学专家。….
New artificial intelligence system can empower medical professionals in diagnosing skin diseases (EurekAlert, March 2020)韩国的研究人员开发了一种基于深度学习的人工智能（AI）算法，能够准确分类皮肤切除疾病，预测恶性程度，提供初步治疗建议，并作为辅助工具，提高临床医生的诊断准确性。…..
AI-assisted diagnosis of skin neoplasms (Dermonaut, Oct 2022)随机对照试验的简短摘要。
Hautscreening-Apps (Stiftung Warentest, Dec 2022)Stiftung Warentest 正在对“皮肤筛查应用程序”进行调查。
Blog
AI in Medicine — Importance of Prospective Study (Medium)AI在实际环境中表现良好至关重要。此外，AI应能够改变医生或患者的决策。然而，由于前瞻性和回顾性研究结果之间的差距相当大，有必要缩小问题范围，并且我们需要付出大量努力来改善数据。….
Contributors
Model Dermatology has been developed with the contribution of many academic researchers worldwide. Seung Seog Han (I Dermatology Clinic) is leading the project. Sung Eun Chang (Asan Medical Center), Jung-Im Na (Seoul National Univ.), Seong Hwan Kim (Hallym Univ.), Myoung Shin Kim (Inje Univ.), Gyeong Hun Park (Hallym Univ.), Soo Ick Cho (Seoul National Univ.), Woohyung Lim (LG AI Research), Ik Jun Moon (Asan Medical Center), Young jae Kim (Asan Medical Center), and Ilwoo Park (Chonnam National Univ.) have contributed to the development of the algorithm since 2016. We are also grateful to Cristian Navarrete-Dechent (Pontificia Universidad Católica de Chile), Camila Muñoz-López (Pontificia Universidad Católica de Chile), Konstantinos Liopyris (Andreas Sygros Hospital; Greece), Roxana Daneshjou (Stanford Univ.; USA), Allan Halpern (Memorial Sloan Kettering Cancer Center; USA), Fortunato Cassalia (Ospedale San Bortolo; Italy), and Luigi Naldi (Ospedale San Bortolo; Italy) who performed the external validation of the algorithm. Mauricio Caceres (Paraguay) and Wail Atra (Sudan) have contributed to translation of the application.
