Computational Pathology in the Era of Emerging Foundation and Agentic AI -- International Expert Perspectives on Clinical Integration and Translational Readiness
This paper addresses the critical problem of integrating advanced AI systems into clinical computational pathology for healthcare professionals, providing a practical assessment of current capabilities and barriers to adoption.
This paper explores the integration of emerging foundation models and agentic AI in computational pathology, identifying the challenges and opportunities for their responsible adoption in clinical practice. It highlights that despite significant performance gains in academic benchmarks for tasks like diagnosis and prognosis, real-world implementation faces economic, technical, and administrative hurdles.
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.