Authors
Xinyu Lu, Qiankun Li, Yakun Gao, Wei Dong, Mengyao Lyu, Siyuan Ma, Jinyue Li, Yufeng Wu, Linghan Cai, Tianyi Zhang, Shangqing Lyu, Zeyu Liu, Hui Liu, Susu Luo
Published in
Journal of translational medicine. Volume 24. Issue 1. Aug 28, 2026. Epub Aug 28, 2026.
Abstract
Digital pathology supports whole-slide imaging, remote review, and computational analysis. Most pathology AI systems, however, remain restricted to predefined tasks. Agentic architectures coordinate perception models, language-based reasoning, external tools, and feedback-dependent actions, but their clinical evidence is derived mainly from retrospective benchmarks and research prototypes.
We review agentic systems in computational pathology using an operational taxonomy based on dynamic control flow, inference-time tool selection, and knowledge integration. We assess architectures, enabling technologies, and applications in diagnosis, prognosis, and therapeutic support. Reported gains are difficult to attribute to agentic organization because studies differ in backbones, training data, and inference budgets. We therefore emphasize validation scope, computational cost, workflow integration, hallucination and security risks, regulatory requirements, patient preferences, and the conditions under which specialist non-agentic models remain preferable.
Agentic architectures have established technical feasibility, but not clinical benefit. Translation should prioritize verifiable tasks, matched comparisons, prospective and external validation, lifecycle governance, and interfaces that preserve pathologist oversight.
PMID:
42665811
Bibliographic data and abstract were imported from PubMed on 29 Aug 2026.
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