Artificial intelligence in digital pathology diagnosis and analysis: technologies, challenges, and future prospects
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.
Authors
- Yuning Sun (ORCID: https://orcid.org/0000-0001-8867-8462)
- Chao-qing Xu
- J D Zhang
- Jie Lei (ORCID: https://orcid.org/0000-0003-2523-5810)
- Lingyun Cheng (ORCID: https://orcid.org/0000-0002-8156-5550)
- Xinfeng Yu
- W Li
- C. Lu
- Nan Pu (ORCID: https://orcid.org/0000-0002-2179-8301)
- H. Wang
- Xi Chen (ORCID: https://orcid.org/0000-0002-8523-3967)
- Qing Chen (ORCID: https://orcid.org/0000-0001-6251-8292)
- Jin-Xing Zhang (ORCID: https://orcid.org/0000-0003-1561-100X)
- Wenjie Liang (ORCID: https://orcid.org/0009-0000-3468-1930)
- Ming-Li Song
- Kaiwen Hu (ORCID: https://orcid.org/0000-0002-1258-9647)
- Linjie Xu (ORCID: https://orcid.org/0000-0003-2212-8438)
- Qiu-Yu Cai
- Meifu Gan (ORCID: https://orcid.org/0000-0002-8143-100X)
- Jian Yang (ORCID: https://orcid.org/0000-0003-4255-9622)
- Jing-Wen Ye
- Heng-Rui Lou
- Zai-Yi Liu
- Sheng-Xu-Ming Zhang
- Kai Yan
- Tian-Hong Gao
- Han Lv
- Xiu-Ming Zhang
- Zun-Lei Feng
- Jia-Bin Xia
Institutions
- Qingdao University (CN)
- Hefei University of Technology (CN)
- National University of Singapore (SG)
- Zhejiang University of Science and Technology (CN)
- University of Trento (IT)
- Wenzhou Medical University (CN)
- Guangzhou First People's Hospital (CN)
- Affiliated Hospital of Qingdao University (CN)
- Guangdong Academy of Medical Sciences (CN)
- Beijing Friendship Hospital (CN)
- Zhejiang Taizhou Hospital (CN)
- Children's Hospital of Fudan University (CN)
- First Affiliated Hospital Zhejiang University (CN)
- Zhejiang University of Technology (CN)
- Zhejiang University (CN)
- Taizhou University (CN)
- Nanjing Medical University (CN)
- Guangzhou Medical University (CN)
Publication Details
- Journal
- Military Medical Research
- Published
- 2026-01-04
- DOI
- https://doi.org/10.1186/s40779-025-00680-6
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Zhejiang University
- National Health Commission of the People's Republic of China
- Fundamental Research Funds for the Central Universities
- Natural Science Foundation of Zhejiang Province