PathoSage: Towards Multi-Source Evidence Adjudication in Pathology via Experience-Aware Agentic Workflow
Researchers have developed advanced AI systems for computational pathology, aiming to improve diagnostic accuracy and reliability. PathoSage and PathPocket are two such frameworks that utilize agentic workflows and multimodal reasoning to process complex evidence, including medical images and text. These systems are designed to mitigate issues like hallucination and context contamination, with PathPocket specifically building a comprehensive pathology evidence corpus and hypergraph to ground its interpretations in verifiable literature. Evaluations show these approaches significantly outperform existing methods and enhance pathologists' diagnostic confidence. AI
IMPACT These advanced AI systems promise to enhance diagnostic accuracy and reliability in pathology, potentially transforming clinical workflows and improving patient outcomes.