Paper
Computational Pathology in the Era of Emerging Foundation and Agentic AI -- International Expert Perspectives on Clinical Integration and Translational Readiness
Authors
Qian Da, Yijiang Chen, Min Ju, Zheyi Ji, Albert Zhou, Wenwen Wang, Matthew A Abikenari, Philip Chikontwe, Guillaume Larghero, Bowen Chen, Peter Neiglinger, Dingrong Zhong, Shuhao Wang, Wei Xu, Drew Williamson, German Corredor, Sen Yang, Le Lu, Xiao Han, Kun-Hsing Yu, Jun-zhou Huang, Laura Barisoni, Geert Litjens, Anant Madabhushi, Lifeng Zhu, Chaofu Wang, Junhan Zhao, Weiguo Hu
Abstract
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.
Metadata
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Raw Data (Debug)
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