Agentic AI in computational pathology remains experimental pending prospective clinical validation
The study reports a comprehensive review of agentic artificial intelligence architectures in computational pathology, evaluating their current applications in diagnosis, prognosis, and therapeutic support. Investigators note that existing evidence relies primarily on retrospective benchmarks and research prototypes, with reported performance gains difficult to isolate from variations in model backbones and training data. The authors emphasize that while technical feasibility is established, clinical benefit remains unproven due to unresolved challenges in workflow integration, hallucination risks, and regulatory compliance. For laboratory and pathology departments, the review underscores the necessity of prospective external validation, lifecycle governance, and maintained pathologist oversight before deploying agentic systems in routine diagnostics.
The original study
Agentic systems in computational pathology: architectures, evidence, and translational challenges.
- Authors
- Lu X, Li Q, Gao Y, Dong W, Lyu M, Ma S, et al.
- Journal
- Journal of translational medicine
- Type
- Journal Article, Review, Research Support, Non-U.S. Gov't
- PMID
- 42665811
Original 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.