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.

翻译：近期，通过基础模型和智能体在人工智能领域取得的突破，加速了计算病理学的发展。学术界在诊断、预后和治疗反应等预测任务的基准数据集上报告的性能提升，已激发了对临床应用的极大热情。尽管发展势头强劲，但实际应用仍显滞后，因为实施过程面临经济、技术和行政方面的挑战。除了现有关于技术架构和性能比较的讨论外，本综述探讨了如何通过将可部署的临床相关性、下游分析能力及其技术成熟度、操作准备度、经济与监管环境相结合，负责任地将这些新兴人工智能系统整合到医疗实践中。基于国际专家组的观点，我们对当前在患者护理环境中的能力与采用障碍进行了实用性评估。