Artificial intelligence in digital pathology: Principles, applications, challenges, and future directions

This article provides an overview of digital pathology and its evolving role in modern diagnostic practice. We begin by comparing conventional microscopy with digital pathology and outlining the major components of the digital pathology workflow. We then explore whole-slide imaging, its imaging modalities, scanner technologies, image storage, and image management systems. We then examine the role of artificial intelligence (AI) in digital pathology, discussing current AI applications and the spectrum of computational approaches, from conventional feature-engineering methods to deep learning and multimodal models. Finally, we review the clinical applications of digital pathology, summarize its implementation across US medical centers, and discuss the technical, regulatory, and operational challenges that must be addressed for broader clinical adoption.