AI and the digital pathology revolution: clinical applications in cancer diagnosis and assessment.

INTRODUCTION: Hematoxylin the transition from classical machine-learning to deep learning models that learn hierarchical representations from raw WSIs; convolutional neural networks, transformers and foundational computational pathology models; tasks such as biomarker prediction and prognostic modeling; emerging research on multimodal AI systems that are integrating histology images with text data to improve clinical relevance; challenges related to data sharing and privacy, generalizability, and the implementation of these approaches in real-world clinical settings. EXPERT OPINION: Digital pathology and AI are transforming cancer diagnosis and evaluation. We expect that AI will be increasingly embedded in routine pathology practice to enhance diagnostic accuracy, improve efficiency, advance biological discovery, and perform tasks out of reach of conventional microscopy, thus advancing precision oncology.