Computational Pathology in the Era of Emerging Foundation and Agentic AI -- International Expert Perspectives on Clinical Integration and Translational Readiness
Year: 2026
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Da, Qian, Chen, Yijiang, Ju, Min, Ji, Zheyi, Zhou, Albert, Wang, Wenwen, Abikenari, Matthew A, Chikontwe, Philip, Larghero, Guillaume, Chen, Bowen, Neidlinger, Peter, Zhong, Dingrong, Wang, Shuhao, Xu, Wei, Williamson, Drew, Corredor, German, Yang, Sen, Lu, Le, Han, Xiao, Yu, Kun-Hsing, Huang, Jun-zhou, Barisoni, Laura, Litjens, Geert, Madabhushi, Anant, Zhu, Lifeng, Wang, Chaofu, Zhao, Junhan, Hu, Weiguo
Institutions: Ruijin Hospital, Stanford Medicine, Stanford University, Qingdao University, University of Chicago, University of Warwick, Carnegie Mellon University, Harvard University, China-Japan Friendship Hospital, Tsinghua University, Emory University, The Wallace H. Coulter Department of Biomedical Engineering, Ant Group (China), Sichuan University, Sichuan University of Science and Engineering, The University of Texas at Arlington, Duke University, Radboud University Nijmegen, Radboud University Medical Center, Radboud Institute for Molecular Life Sciences
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7134277491