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

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 

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

Commentary

  1. Toward Augmented Intelligence: The First Prospective, Randomized Clinical Trial Assessing Clinician and Artificial Intelligence Collaboration in Dermatology – J Invest Dermatol. 2022 
  2. Automated Classification of Skin Lesions: From Pixels to Practice – J. Invest Dermatol. 2018
  3. Problems and Potentials of Automated Object Detection for Skin Cancer Recognition – JAMA Dermatol. 2020

Magazine

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。….

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。…..

随机对照试验的简短摘要。

Stiftung Warentest 正在对“皮肤筛查应用程序”进行调查。

Blog

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是在许多学术研究人员的共同贡献下开发的。项目由Seung Seog Han 领导。自2016年以来,Sung Eun Chang, Jung-Im Na, Seong Hwan Kim, Myoung Shin Kim, Gyeong Hun Park, Soo Ick Cho, Woohyung Lim, Ik Jun Moon, Young jae Kim, 以及 ilwoo Park为算法的开发做出了贡献。我们还要感谢Cristian Navarrete-Dechent‬, Konstantinos Liopyris, Roxana Daneshjou和Allan Halpern进行算法的外部验证。