Abstract
The expanding shift towards digital pathology in clinical practice globally highlights its potential to enhance patient care through artificial intelligence (AI)-powered, computer-assisted diagnostics. Effective communication between AI scientists and pathologists is crucial for this transformation, though their differing technical languages can pose challenges. The manuscript aims to offer simplified explanations of common AI terminology, along with practical examples and illustrations, to help pathologists better grasp AI concepts. This review is divided into the following sections: AI technologies and algorithms in computational pathology; frameworks for training AI models; nomenclature of image analysis; and public datasets for computational pathology research. These sections collectively provide a comprehensive understanding of the current landscape and resources in computational pathology. The manuscript fosters better communication between these fields and showcases the advantages of AI technologies in pathology.