Discordance, accuracy and reproducibility study of pathologists' diagnosis of melanoma and melanocytic tumors.

Accurate melanoma diagnosis is crucial for patient outcomes and reliability of AI diagnostic tools. We assess interrater variability among eight expert pathologists reviewing histopathological images and clinical metadata of 792 melanoma-suspicious lesions prospectively collected at eight German hospitals. Moreover, we provide access to the largest panel-validated dataset featuring dermoscopic and histopathological images with metadata.

Is Kaposi sarcoma a novel comorbidity of cutaneous lymphoma? A systematic review of the literature.

Background And Objectives: Patients with cutaneous lymphomas (CL) are at an increased risk of developing secondary malignancies. This study aimed to assess the frequency of association between CL and Kaposi sarcoma (KS) and to identify factors that may promote the co-occurrence of these two diseases.

Patients And Methods: On January 25, 2024, we conducted a systematic search of four electronic medical databases to identify all published cases of KS associated with CL.

Federated Learning for Decentralized Artificial Intelligence in Melanoma Diagnostics.

Importance: The development of artificial intelligence (AI)-based melanoma classifiers typically calls for large, centralized datasets, requiring hospitals to give away their patient data, which raises serious privacy concerns. To address this concern, decentralized federated learning has been proposed, where classifier development is distributed across hospitals.

Objective: To investigate whether a more privacy-preserving federated learning approach can achieve comparable diagnostic performance to a classical centralized (ie, single-model) and ensemble learning approach for AI-based melanoma diagnostics.