The decision of which method to use should be based on clinic size, information desired, and clinic resources for ease of applying either of the methods in clinical practice. Each of these methodologies has their limitations and the question remains as to which method best reflects the quality of anticoagulation management. Regardless of these limitations, investigators are urged to employ at least one method of measuring the quality of oral anticoagulation management so as to better assess the validity of the clinical outcomes.

Background: In recent studies, convolutional neural networks (CNNs) outperformed dermatologists in distinguishing dermoscopic images of melanoma and nevi. In these studies, dermatologists and artificial intelligence were considered as opponents. However, the combination of classifiers frequently yields superior results, both in machine learning and among humans. In this study, we investigated the potential benefit of combining human and artificial intelligence for skin cancer classification. Methods: Using 11,444 dermoscopic images, which were divided into five diagnostic categories, novel deep learning techniques were used to train a single CNN. Then, both 112 dermatologists of 13 German university hospitals and the trained CNN independently classified a set of 300

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