Digital Pathology Foundation Models: Advancing Computational Diagnostics in Precision Oncology

Abstract

Digital pathology has emerged as one of the most transformative technologies in modern oncology, enabling comprehensive computational analysis of histopathological specimens at unprecedented scale and resolution. Recent advances in artificial intelligence (AI), particularly foundation models pretrained on large-scale whole-slide image datasets, have revolutionized computational pathology by enabling generalized representation learning across diverse cancer types. Unlike conventional deep learning models trained for individual diagnostic tasks, digital pathology foundation models acquire transferable histomorphological knowledge through self-supervised learning and can subsequently be adapted to tumor classification, grading, biomarker prediction, molecular characterization, prognostic assessment, therapeutic response prediction, and clinical decision support. Integration of foundation AI models with radiological imaging, genomics, transcriptomics, proteomics, metabolomics, laboratory biomarkers, and longitudinal clinical information has further accelerated precision oncology by enabling multimodal computational diagnostics. Advances in transformer architectures, graph neural networks, multimodal learning, large language models, retrieval-augmented generation, agentic AI, and generative artificial intelligence continue to expand the capabilities of digital pathology. Nevertheless, important challenges remain regarding data standardization, computational scalability, explainability, interoperability, cybersecurity, regulatory validation, and equitable implementation. This review provides a comprehensive overview of digital pathology foundation models, highlighting computational principles, current clinical applications, implementation challenges, and future perspectives for advancing computational diagnostics in precision oncology.[1]