Computational Pathology in the Era of Emerging Foundation and Agentic AI -- International Expert Perspectives on Clinical Integration and Translational Readiness

TL;DR

This paper reviews recent advances in AI-driven computational pathology, emphasizing the gap between technological progress and clinical adoption, and discusses practical challenges and strategies for responsible integration into healthcare.

Contribution

It offers an international expert perspective on the current state, barriers, and readiness of AI systems for clinical implementation in pathology.

Findings

AI performance gains in diagnosis, prognosis, and treatment response

Implementation challenges include economic, technical, and regulatory barriers

Practical assessment of capabilities and barriers in clinical settings

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.