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Artificial intelligence in digital pathology diagnosis and analysis:technologies,challenges,and future prospects 认领 引用
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作者 Xiu-Ming Zhang Tian-Hong Gao +27 位作者 Qiu-Yu Cai Jia-Bin Xia Yu-Ning Sun Jian Yang Wei-Han Li Sheng-Xu-Ming Zhang Heng-Rui Lou Xiao-Tian Yu Kai-Wen Hu Jing-Wen Ye Jin-Xing Zhang Jie Lei Le-Chao Cheng Lin-Jie Xu Qing Chen He-Xiang Wang Mei-Fu Gan Cheng Lu Nan Pu Ming-Li Song Xin Chen Wen-Jie Liang Han Lv Chao-Qing Xu Zai-Yi Liu Jing Zhang Kai Yan Zun-Lei Feng 《Military Medical Research》 SCIE CAS CSCD 2026年第6期991-1029,共39页
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 deve... 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. 展开更多
关键词 Artificial intelligence(AI) Pathology images Quantitative feature Pathology foundation model
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