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AI-Powered histopathology slide image interpretation in oncology: A comprehensive knowledge mapping and bibliometric
1Faculty of Medicine and Allied Medical Sciences, An-Najah National University, Nablus, Palestine.
Objectives:
To map global research on AI-driven histopathological image interpretation in oncology (2000-2024).
Methodology:
Scopus-based bibliometrics using Boolean queries; data exported, cleaned, and analyzed in VOSviewer.
Results:
The dataset included 1874 publications, surging after 2015. China (n = 561) and the United States (n = 467) led output; leading institutions included Harvard Medical School and the Chinese Academy of Sciences. Top journals were Scientific Reports, Cancers, and IEEE Access; the corpus H-index was 112. Collaboration networks intensified, especially U.S.-Asia. Keyword mapping showed four clusters: (1) deep learning for breast cancer/CNN diagnostics; (2) transfer learning/feature extraction; (3) prognostic modeling/tumor microenvironment; and (4) digital infrastructure/explainable AI. Overlay analyses traced a shift from classical machine learning to transformers and multimodal models integrating molecular and clinical data; emerging themes include semantic segmentation, self-supervised learning, and therapy-response prediction. Applications spanned breast, prostate, colorectal, head-neck, gynecologic, and gastrointestinal/liver cancers. Models primarily used whole-slide images (e.g., TCGA) and multi-omics; algorithms included CNNs, deep learning, classical machine learning, and weakly supervised approaches. Evidence ranged from proof-of-concept to multicenter validation and workflow integration; adoption remains constrained by data standardization, interpretability, and regulation. Clinically, AI improved diagnostic accuracy/efficiency and supported personalization via multi-omics. Bibliographic coupling revealed three clusters: clinical/translational journals; engineering/computational outlets; and interdisciplinary venues linking algorithmic innovation with digital pathology.
Conclusions:
AI histopathology is advancing toward clinical-grade deployment, propelled by collaboration and methodological innovation, yet limited by data standards, explainability, and regulatory requirements.
