Foundation and multimodal models have achieved great performance across many diagnostic tasks in pathology, yet only a few AI systems have entered routine clinical practice. This gap between research capability and real-world deployment is defined as the adoption paradox of computational pathology. The review systematically examines the evolution of pathology AI from task-specific deep learning to foundation, multimodal, and agentic systems, while identifying four major categories of clinically deployed products. Using a three-stage maturity framework consisting of algorithmic capability, system integration, and institutional adoption, the study highlights three key barriers limiting clinical translation: data and infrastructure fragility, workflow misalignment, and institutional trust deficits. The authors further propose infrastructure-first AI, workflow-embedded intelligence, and adaptive governance as potential pathways toward sustainable clinical integration. The review provides practical guidance for translating pathology AI from research prototypes into routine clinical use.
LabMed Discovery
News article
Adoption paradox of artificial intelligence in computational pathology: a three-stage maturity model from algorithms to clinical integration
2-Jun-2026