The Evolution of Digital Pathology in Infrastructure, Artificial Intelligence and Clinical Impact

Digital pathology has rapidly evolved from a technical innovation to a foundational component of modern diagnostic practice.The digitization of histologic and cytologic slides through whole-slide imaging has enabled remote interpretation, streamlined workflows, and unprecedented opportunities for computational analysis.This review explores the current landscape of digital pathology, examining the integration of imaging platforms, workflow automation systems, tiered data storage, and emerging cloud infrastructure.Particular attention is given to the convergence between digital pathology and artificial intelligence (AI), focusing on the transition from traditional machine learning and weakly supervised methods to more recent advances in multimodal foundation models and generative AI.These state-of-the-art systems offer enhanced capabilities in image understanding, diagnostic assistance, prognostic prediction, and decision support.Rather than replacing expert judgment, AI is increasingly positioned to augment the interpretive work of the pathologist, a concept aligned with augmented intelligence.In addition to technical advances, this review addresses critical challenges surrounding data governance, regulatory frameworks, algorithmic transparency, and equitable clinical deployment.The utility of AI in thyroid pathology is presented as a representative use case, illustrating both the potential and limitations of real-world implementation.As digital pathology continues to mature, its successful integration into routine clinical workflows will require technological innovation, interdisciplinary collaboration, ethical oversight, and evidencebased validation to ensure safe, sustainable, and impactful use in contemporary medicine.