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| Format: | Preprint |
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2026
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| Online-Zugang: | https://arxiv.org/abs/2603.05884 |
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| _version_ | 1866908879947825152 |
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| author | 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 |
| author_facet | 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 |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_05884 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Computational Pathology in the Era of Emerging Foundation and Agentic AI -- International Expert Perspectives on Clinical Integration and Translational Readiness 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 Computational Engineering, Finance, and Science Artificial Intelligence 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. |
| title | Computational Pathology in the Era of Emerging Foundation and Agentic AI -- International Expert Perspectives on Clinical Integration and Translational Readiness |
| topic | Computational Engineering, Finance, and Science Artificial Intelligence |
| url | https://arxiv.org/abs/2603.05884 |