AI助力病理诊断，但落地仍受技术与成本制约
Computational Pathology in the Era of Emerging Foundation and Agentic AI -- International Expert Perspectives on Clinical Integration and Translational Readiness
- 结合国际专家意见，评估基础模型与智能体在病理中的临床应用潜力
- 当前AI在诊断、预后等任务中表现优异，但真实场景部署仍困难
- 关注技术成熟度、经济性与监管环境，为临床整合提供实用指南
近年来，基础模型与智能体的突破加速了计算病理学的发展。学术界在基准数据集上报告了诊断、预后和治疗反应预测任务中的显著性能提升，激发了对临床应用的浓厚兴趣。尽管发展势头强劲，现实应用仍滞后，因实施面临经济、技术和管理挑战。本文超越现有技术架构与性能对比讨论，从可部署的临床相关性出发，连接下游分析能力与技术成熟度、运营准备度、经济与监管背景，基于国际专家视角，提供对患者诊疗环境中当前能力与采纳障碍的实用评估。
原文摘要 · Abstract (English)
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.
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