Artificial intelligence in digital pathology diagnosis and analysis: technologies, challenges, and future prospects
Xiu-Ming Zhang, Tian-Hong Gao, 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
TL;DR
This paper reviews how AI can transform pathology by analyzing histopathological images, covering technologies, challenges, and future directions for better disease diagnosis.
Contribution
The paper provides a systematic evaluation of AI applications in pathology and proposes a roadmap for advancing precision oncology.
Findings
AI tools show promise in tumor classification and biomarker discovery in pathology.
Challenges include noisy annotations and domain shifts in clinical translation of AI models.
A technical taxonomy and benchmarking of algorithms are presented for diverse diagnostic tasks.
Abstract
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,…
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Taxonomy
TopicsAI in cancer detection · Radiomics and Machine Learning in Medical Imaging · Digital Imaging for Blood Diseases