image: A three-stage maturity model for clinical translation of pathology AI, linking structural barriers to system level pathways. The model organizes the translational journey from research prototype to sustained clinical use into three sequential stages, each gated by specific barriers (analyzed in Section 3) and addressed by corresponding pathways (Section 4). Stage 1: Algorithmic capability (gated by retrospective benchmark evidence). Barrier: data and infrastructure fragility (scanner shift, format fragmentation, manual QC). Pathway: infrastructure first AI (vendor-neutral formats, automated QC, multicenter baselines, federated learning). Stage 2: System integration (gated by prospective multisite validation). Barrier: workflow and human-system misalignment (cognitive rhythm, automation bias, scenario-dependent latency). Pathway: workflow embedded intelligence (triage, assistive layers, uncertainty visualization, frictionless human override). Stage 3: Institutional adoption (gated by demonstrable workflow benefit, viable reimbursement, and a documented governance plan). Barrier: institutional trust and governance constraints (interpretability, validation gaps, liability, generative AI risks). Pathway: adaptive governance [machine learning operations (MLOps), shadow deployment, real-world evidence, PCCPs for continuous learning]. The bottom row maps representative approved and research products (from Table 1) to their current dominant maturity stage as of early 2026. This framework enables systematic diagnosis of why a given AI system fails to reach clinical practice and what intervention is required to advance it to the next stage.
Credit: Lu Cai, Biwen Meng, Jie Huang, Guanyu Ding, Min Ju, Wenwen Wang, Shijie Deng, Liqin Lai, Jin Wang, Chunxue Yang, Miao Ruan, Shugong Xu, Chaofu Wang, Jingxin Liu, Qian Da.
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.
Journal
LabMed Discovery
Method of Research
News article
Article Title
Adoption paradox of artificial intelligence in computational pathology: a three-stage maturity model from algorithms to clinical integration
Article Publication Date
2-Jun-2026