PathoSage: Towards Multi-Source Evidence Adjudication in Pathology via Experience-Aware Agentic Workflow
PathoSage introduces an experience-aware agentic workflow for multi-source evidence adjudication in pathology. This new approach aims to streamline the complex process of evaluating diverse data in pathological diagnoses.
A new paper titled "PathoSage: Towards Multi-Source Evidence Adjudication in Pathology via Experience-Aware Agentic Workflow" has been published. The paper, authored by Chengyang Zhang and seven other researchers, was released on May 18, 2026. This research introduces an innovative approach to handling complex data in pathological diagnoses. It focuses on the critical task of integrating and evaluating evidence from various sources.
PathoSage proposes an "experience-aware agentic workflow." This system is designed to streamline the adjudication of multi-source evidence in pathology, making the process more efficient and reliable. The methodology aims to improve the accuracy and consistency of diagnoses by leveraging an intelligent, automated framework.
The paper is accessible through arXiv, under the computer science category, specifically cs.AI. It is available in PDF format, and experimental HTML and TeX source views are also provided. The article is part of the ongoing advancements in artificial intelligence applications within the medical field.
Additional resources, such as bibliographic tools, code repositories like alphaXiv and Hugging Face, and demo platforms like Replicate, are linked to the paper. These resources allow for further exploration and interaction with the research. The publication underscores the growing collaboration between AI and pathology to enhance diagnostic processes.
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