Deep learning-based computational pathology: Technologies, clinical applications, and future directions

The adoption of whole-slide imaging is establishing a new paradigm in digital pathology. However, the translation of artificial intelligence (AI) from research to clinical practice faces significant hurdles, largely due to a misalignment between algorithmic advances and the practical demands of pathological diagnosis and prognosis. In this review, we propose a dual-perspective framework to systematically bridge this gap by linking core clinical tasks with cutting-edge deep learning methodologies. We present a comprehensive overview of the field from 2020 to 2025, analyzing how architectures such as convolutional neural networks, vision transformers, and graph neural networks are being adapted for diagnostic classification, tissue segmentation, and prognostic prediction. A key contribution is our novel algorithm-clinical task mapping framework, which offers practical guidance for selecting and designing AI solutions tailored to specific clinical goals. We also highlight emerging trends that minimize reliance on costly annotations-including weakly supervised and self-supervised learning-as well as advances in predicting immunohistochemistry results directly from hematoxylin and eosin-stained slides. Finally, we address critical challenges related to model interpretability, regulatory approval, and multicenter generalization, and outline a future pathway focused on developing integrated, trustworthy, and equitable AI systems that enhance, rather than replace, the expertise of pathologists.