Agentic systems in computational pathology: architectures, evidence, and translational challenges
Abstract
Background: 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.
Main body: 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.
Conclusions: 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.
Read the paper
Related studies
- Knowledge Management Perspective of Generative Artificial Intelligence
- Navigating Generative Artificial Intelligence Promises and Perils for Knowledge and Creative Work
- Characterizing the evolving research on enterprise content management
- Large language models and multimodal foundation models for precision oncology
- From Generative AI to Extended Reality: Multidisciplinary Perspectives on the Challenges, Opportunities, and Future of Educational Computing
- Generative Artificial Intelligence: A New Engine for Advancing Environmental Science and Engineering
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.