Trust but Verify:Evidence-Linked Multi-Agent Clinical Information Extraction in Pathology
Clinical feature extraction from pathology reports is challenging because relevant evidence may be distributed across coded and narrative fields and depend on specimen attribution, negation, ancillary findings, and diagnostic context. We retrospectively evaluated the NimbleMind Multi-Agent System (nMAS), a configurable workflow that separates clinician-defined field specifications from extraction models and returns report-level predictions with source-linked evidence. The study included 54 dummy gastric biopsy pathology reports from Singapore and four binary target fields, yielding 216 feature-case decisions. nMAS correctly classified 213 of 216 decisions (98.61\%), and all evidence spans...
| Authors | Yufan Wang, Anit Kumar Sahu, Yan Fei Ng et al. |
|---|---|
| Primary category | cs.AI |
| Published | 7 July 2026 |
| Last updated at source | 1 August 2026 |
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