Driven by technologies such as whole-slide imaging (WSI), spatial omics, and self-supervised pretraining, digital-intelligent pathology (DIP) is shifting pathology from traditional experience-based diagnosis toward a new data-intelligence-driven engineering paradigm. Building on these advances, DIP is establishing an integrated engineering system tailored to clinical and pharmaceutical needs, thereby forming a closed loop of diagnosis‒treatment‒Response. This study outlines the technological evolution of DIP, focusing on large-scale self-supervised foundation models, cross-modal virtual staining, pathology‒omics alignment, and tumor microenvironment (TME) modeling. It also outlines DIP's core functions: automated grading, multi-task learning, TME characterization, and genotype-phenotype mapping. Through examples in target discovery, mechanism studies, toxicity assessment, and precision trials, the study demonstrates how DIP enables functional phenotype mining, improves efficacy prediction and patient stratification, and delivers clinical and industrial value. Moreover, it explores challenges regarding data standardization, cross-center generalization, interpretability, and regulatory governance, and envisions DIP evolving, through multimodal and trustworthy artificial intelligence, into a sustainable "diagnosis‒treatment‒Response" loop. For the development of national biopharmaceutical and health systems in China, DIP is positioned as a "core hub for translational medical engineering," steadily advancing pathology from a diagnostic science to a therapeutic engine. This suggests that DIP could become an important link between clinical decision-making and drug R&D, bolstering original innovation and R&D efficiency of China's biopharmaceutical industry.