Xiu-Ming Zhang, Tian-Hong Gao, Qiu-Yu Cai, Jia-Bin Xia, Yuning Sun, Jian Yang, W Li, Sheng-Xu-Ming Zhang, Heng-Rui Lou, Xinfeng Yu, Kaiwen Hu, Jing-Wen Ye, Jin-Xing Zhang, Jin-Xing Zhang, Jie Lei, Le-Chao Cheng, Linjie Xu, Qing Chen, He-Xiang Wang, Meifu Gan, Nan Pu, Nan Pu, Ming-Li Song, Xin Chen, Wen-Jie Liang, Han Lv, Chao-qing Xu, Zai-Yi Liu, Jing Zhang, Jing Zhang, Kai Yan, Zunlei Feng
Artificial intelligence (AI) offers transformative potential in pathology, where histopathological images remain the diagnostic gold standard due to their rich morphological and molecular information. While the rapid development of AI-driven computational pathology tools is revolutionizing disease interpretation, these technologies have not yet been systematically evaluated. Therefore, this review systematically evaluates AI applications across the diagnostic continuum, from image preprocessing and tumor classification to prognostic stratification and the discovery of predictive biomarkers. It presents a technical taxonomy of the algorithms and foundation models powering these applications, benchmarking their performance across diverse diagnostic tasks through rigorous comparative analyses. It also identifies critical challenges in clinical translation, including computational scaling, noisy annotations, interpretability gaps, and domain shifts. Finally, it proposes a roadmap for advancing AI applications in precision oncology and pathological research. By bridging technological innovation with clinical needs, this review aims to accelerate the integration of robust, unified, scalable AI solutions into diagnostic workflows.