AI Transparency in Digital Pathology

Dear Colleagues,

In recent years, machine learning (ML) and deep learning (DL) have permeated the digital pathology field. ML and DL tools specifically developed for whole-slide image (WSI) analysis may enhance the diagnostic process in many fields of human pathology and allow for more consistent results, providing valid support for detecting multiple biomarkers that expert pathologists miss.

Despite all these advantages and promising results, the introduction of these tools in clinical practice needs to be revised. The reproducibility of DL models applied to WSI analysis and the absence of explainability and interpretability of these models, which appear as “black boxes”, represent crucial points, and often a barrier, for the transition from research to clinical workflows. It is time to rethink the approach of artificial intelligence to pathology, aiming to reach high levels of reproducibility and explainability.

This Special Issue aims to cover recent advances in digital pathology by collecting papers that make proposals of a high-quality, robust, easy-to-use, and transparent processing pipeline, which can help ensure the validity and the explainability of AI models applied to histopathology in clinical workflows.

Dr. Matteo Fraschini
Dr. Gavino Faa
Guest Editors

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