Computational Pathology in the Era of Emerging Foundation and Agentic AI -- International Expert Perspectives on Clinical Integration and Translational Readiness
🤖 AI Summary
Although foundation models and agentic AI have demonstrated strong performance in computational pathology, their clinical implementation remains constrained by technical, economic, and organizational factors. This study is the first to systematically evaluate their integration pathways into real-world clinical tasks—including pathological diagnosis, prognosis prediction, and treatment response assessment—within a multidimensional framework grounded in international expert consensus. By incorporating perspectives from clinical workflows, regulatory policies, and health economics, the project identifies critical bottlenecks in authentic healthcare settings and proposes integration strategies that balance technical capabilities with clinical feasibility. The resulting framework offers practical guidance and evaluation criteria to support responsible and sustainable deployment of AI in pathology.
Application Category
📝 Abstract
Recent breakthroughs in artificial intelligence through foundation models and agents have accelerated the evolution of computational pathology. Demonstrated performance gains reported across academia in benchmarking datasets in predictive tasks such as diagnosis, prognosis, and treatment response have ignited substantial enthusiasm for clinical application. Despite this development momentum, real world adoption has lagged, as implementation faces economic, technical, and administrative challenges. Beyond existing discussions of technical architectures and comparative performance, this review considers how these emerging AI systems can be responsibly integrated into medical practice by connecting deployable clinical relevance with downstream analytical capabilities and their technical maturity, operational readiness, and economic and regulatory context. Drawing on perspectives from an international group, we provide a practical assessment of current capabilities and barriers to adoption in patient care settings.
Problem
Research questions and friction points this paper is trying to address.
Computational Pathology
Foundation Models
Agentic AI
Clinical Integration
Translational Readiness
Innovation
Methods, ideas, or system contributions that make the work stand out.
foundation models
agentic AI
computational pathology
clinical integration
translational readiness
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