PathoSage: Towards Multi-Source Evidence Adjudication in Pathology via Experience-Aware Agentic Workflow

Researchers have developed a framework called PathoSage, designed to improve decision-making in pathology through the use of language models and agent workflows. Although multimodal language models have shown promising results in this field, they still face challenges in reasoning reliably at the patch level. PathoSage is divided into three stages: knowledge retrieval, evidence collection, and evidence evaluation. Structured evidence evaluation is a key component that independently analyzes heterogeneous evidence from tools and generates a final judgment in a fresh context, reducing anchor bias. Additionally, a Beta-Bernoulli-based experience system is introduced, modeling long-term tool reliability and constructing weighted priors by similarity for future tool use. Experiments have shown that PathoSage can effectively mitigate hallucinations and classifier disagreements, outperforming existing pathology models and agents. This news is significant because it highlights how explicit evidence evaluation and tool reliability modeling can be crucial for developing robust and reliable pathology agents, which in turn can improve diagnostic accuracy and disease treatment. The ability to develop such systems could have a significant impact on medicine and biomedical research.

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