PathoSage: Towards Multi-Source Evidence Adjudication in Pathology via Experience-Aware Agentic Workflow
arXiv:2606.07549v1 Announce Type: new Abstract: Recent advances in Multimodal Large Language Models (MLLMs) and agent workflows have shown strong promise for computational pathology, yet reliable patch-level reasoning remains challenging. End-to-end pathology MLLMs often hallucinate morphological features, while recent agentic systems usually merge tool outputs and retrieved knowledge into a shared context, making decisions vulnerable to conflicting evidence and context contamination. We propose PathoSage, a three-stage framework that explicitly separates knowledge retrieval, evidence collecti
The rapid advancement of Multimodal Large Language Models (MLLMs) and agent workflows is pushing the boundaries of AI applications in specialized fields like computational pathology.
This development addresses critical limitations in AI for sensitive domains by improving reliability, reducing hallucination, and mitigating context contamination in complex decision-making processes.
AI systems can now process multi-source evidence more robustly, which could lead to more accurate and trustworthy automated diagnostics in pathology, potentially accelerating research and clinical workflows.
- · AI developers
- · Healthcare providers
- · Pathology labs
- · Patients
- · Traditional diagnostic methods
- · AI systems lacking robust evidence adjudication
PathoSage improves the reliability and accuracy of AI diagnostics in computational pathology.
Increased trust in AI-driven pathology could lead to wider adoption and integration into clinical practice, potentially streamlining diagnosis and treatment planning.
The methodology of robust evidence adjudication could be generalized to other critical AI applications, accelerating the development of reliable AI agents across various sensitive domains beyond medicine.
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Read at arXiv cs.AI