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🧠 AI⚪ NeutralImportance 5/10
Computational Pathology in the Era of Emerging Foundation and Agentic AI -- International Expert Perspectives on Clinical Integration and Translational Readiness
arXiv – CS AI|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|
🤖AI Summary
This academic review examines the integration of foundation models and AI agents in computational pathology for medical applications. While AI shows promising performance in diagnosis and treatment prediction tasks, real-world clinical adoption remains limited due to economic, technical, and regulatory challenges.
Key Takeaways
- →Foundation models and AI agents are accelerating computational pathology development with strong benchmark performance in diagnosis and prognosis tasks.
- →Despite technical advances, real-world clinical adoption of AI pathology systems remains slow due to implementation barriers.
- →Economic, technical, and administrative challenges are the primary obstacles preventing widespread deployment in healthcare settings.
- →The review emphasizes responsible AI integration by connecting clinical relevance with technical maturity and operational readiness.
- →International expert perspectives highlight the gap between academic performance and practical healthcare implementation.
#ai#healthcare#pathology#foundation-models#clinical-ai#medical-diagnosis#ai-adoption#regulatory#implementation
Read Original →via arXiv – CS AI
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