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Source note2 min read

Finding H. pylori in the Fine Print: Evidence-Linked Multi-Agent Case Finding from Gastric Biopsy Reports

Source

arXiv

arxiv.org/abs/2607.06435v1

Read the source

Source

  • Source name: arXiv
  • Source kind: paper
  • Authors / organization: Yufan Wang, Anit Kumar Sahu, Yan Fei Ng, Daniel Kang, Shayan Vassef, Soorya Ram Shimgekar, Koustuv Saha, Piyum Zonooz, Navin Kumar, Chee Leong Cheng, Li Yan Khor
  • Venue: arXiv: cs.AI
  • Published: 2026-07-07
  • Collected: 2026-07-08
  • URL: http://arxiv.org/abs/2607.06435v1
  • PDF: https://arxiv.org/pdf/2607.06435v1
  • DOI: UNKNOWN

Plain-Language Brief

Researchers evaluated a system called nMAS that can automatically extract information about Helicobacter pylori infection from gastric biopsy reports. The system had an accuracy of 98.61% in classifying four binary fields related to the infection, and could potentially save significant time for healthcare professionals by automating the review process.

What the Document Says

  • The study is a retrospective pilot evaluation of the Nimblemind Multi-Agent System (nMAS) on 54 de-identified gastric biopsy pathology reports.
  • Four clinician-scoped binary fields were evaluated: gastric/stomach biopsy, biopsy status, H. pylori positivity, and H. pylori-associated gastritis.
  • The system correctly classified 213 out of 216 feature-case decisions, corresponding to 98.61% overall accuracy.
  • A separately implemented UMA-style MiniMax M2.5 comparator produced similar results.
  • The study demonstrates the contribution of workflow integration and traceability, not predictive superiority.
  • Under an illustrative scenario, nMAS could save 81.9 staff-hours and about USD~6,100 in potential staff-time value by reviewing 1,000 reports at five seconds per evidence-linked verification instead of five minutes per manual review.
  • The authors suggest that larger multi-institutional studies should evaluate evidence-span correctness, clinician verification time, and generalizability.

Key Concepts Mentioned

  • Helicobacter pylori
  • gastric biopsy
  • pathology reports
  • nMAS
  • binary fields
  • overall accuracy
  • staff-hours
  • staff-time value

Claims Made by the Document

  • nMAS correctly classified 98.61% of the cases.
  • Under an illustrative scenario, nMAS could save significant time and money.

Evidence Used

  • The study reports the number of correct classifications by nMAS.
  • The study presents a hypothetical scenario and its potential cost savings.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

Data from Singapore indicated that about 31% of the population had evidence of Helicobacter pylori infection. Persistent H. pylori infection is associated with chronic active gastritis and peptic ulcer disease, and its eradication is key to gastric cancer prevention. However, evidence supporting \textit{H. pylori} positivity and H. pylori-associated gastritis may be distributed across heterogeneous coded and free-text report fields and may require contextual interpretation of assertion and negation, limiting keyword search, and making manual review difficult to scale. We conducted a retrospective pilot evaluation of the Nimblemind Multi-Agent System (nMAS), a field-name-driven, evidence-linked extraction workflow, using 54 de-identified gastric biopsy pathology reports from a large healthcare system in Singapore. Four clinician-scoped binary fields were evaluated: gastric/stomach biopsy, biopsy status, H. pylori positivity, and H. pylori-associated gastritis. Across 216 feature-case decisions, nMAS correctly classified 213, corresponding to 98.61% overall accuracy. A separately implemented UMA-style MiniMax M2.5 comparator produced similar aggregate and per-field classification metrics. Although predictive performance was similar, nMAS maintained unified report-level outputs with supporting source sentences; the demonstrated contribution is therefore workflow integration and traceability rather than predictive superiority. Under an illustrative, unmeasured scenario, reviewing 1,000 reports at five minutes per manual review versus five seconds per evidence-linked verification would reduce review time from 83.3 to 1.4 staff-hours, corresponding to 81.9 staff-hours and about USD~6,100 in potential staff-time value. Larger multi-institutional studies should evaluate evidence-span correctness, clinician verification time, and generalizability.