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Performance Evaluation of Open-Source Large Language Models for Assisting Pathology Report Writing in Japanese

Masataka Kawai, Singo Sakashita, Shumpei Ishikawa, Shogo Watanabe, Anna Matsuoka, Mikio Sakurai, Yasuto Fujimoto, Yoshiyuki Takahara, Atsushi Ohara, Hirohiko Miyake, Genichiro Ishii · Mar 12, 2026 · Citations: 0

Expert Verification Medicine Pairwise Preference
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How to use this page

Low trust

Use this as background context only. Do not make protocol decisions from this page alone.

Best use

Background context only

What to verify

Read the full paper before copying any benchmark, metric, or protocol choices.

Evidence quality

Low

Derived from extracted protocol signals and abstract evidence.

Abstract

The performance of large language models (LLMs) for supporting pathology report writing in Japanese remains unexplored. We evaluated seven open-source LLMs from three perspectives: (A) generation and information extraction of pathology diagnosis text following predefined formats, (B) correction of typographical errors in Japanese pathology reports, and (C) subjective evaluation of model-generated explanatory text by pathologists and clinicians. Thinking models and medical-specialized models showed advantages in structured reporting tasks that required reasoning and in typo correction. In contrast, preferences for explanatory outputs varied substantially across raters. Although the utility of LLMs differed by task, our findings suggest that open-source LLMs can be useful for assisting Japanese pathology report writing in limited but clinically relevant scenarios.

Low-signal caution for protocol decisions

Use this page for context, then validate protocol choices against stronger HFEPX references before implementation decisions.

  • The available metadata is too thin to trust this as a primary source.
  • The abstract does not clearly describe the evaluation setup.
  • The abstract does not clearly name benchmarks or metrics.
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Should You Rely On This Paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Best use

Background context only

Use if you need

Background context only.

Main weakness

The available metadata is too thin to trust this as a primary source.

Trust level

Low

Usefulness score

40/100 • Low

Treat as adjacent context, not a core eval-method reference.

Human Feedback Signal

Detected

Evaluation Signal

Weak / implicit signal

Usefulness for eval research

Adjacent candidate

Extraction confidence 45%

If you are doing eval pipeline work, start here:

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What We Could Verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

partial

Pairwise Preference, Expert Verification

Directly usable for protocol triage.

"The performance of large language models (LLMs) for supporting pathology report writing in Japanese remains unexplored."

Evaluation Modes

missing

None explicit

Validate eval design from full paper text.

"The performance of large language models (LLMs) for supporting pathology report writing in Japanese remains unexplored."

Quality Controls

missing

Not reported

No explicit QC controls found.

"The performance of large language models (LLMs) for supporting pathology report writing in Japanese remains unexplored."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"The performance of large language models (LLMs) for supporting pathology report writing in Japanese remains unexplored."

Reported Metrics

missing

Not extracted

No metric anchors detected.

"The performance of large language models (LLMs) for supporting pathology report writing in Japanese remains unexplored."

Rater Population

partial

Domain Experts

Helpful for staffing comparability.

"In contrast, preferences for explanatory outputs varied substantially across raters."

Human Feedback Details

  • Uses human feedback: Yes
  • Feedback types: Pairwise Preference, Expert Verification
  • Rater population: Domain Experts
  • Expertise required: Medicine

Evaluation Details

  • Evaluation modes:
  • Agentic eval: None
  • Quality controls: Not reported
  • Evidence quality: Low
  • Use this page as: Background context only

Protocol And Measurement Signals

Benchmarks / Datasets

No benchmark or dataset names were extracted from the available abstract.

Reported Metrics

No metric terms were extracted from the available abstract.

Research Brief

Metadata summary

The performance of large language models (LLMs) for supporting pathology report writing in Japanese remains unexplored.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key Takeaways

  • The performance of large language models (LLMs) for supporting pathology report writing in Japanese remains unexplored.
  • We evaluated seven open-source LLMs from three perspectives: (A) generation and information extraction of pathology diagnosis text following predefined formats, (B) correction of typographical errors in Japanese pathology reports, and (C) subjective evaluation of model-generated explanatory text by pathologists and clinicians.
  • Thinking models and medical-specialized models showed advantages in structured reporting tasks that required reasoning and in typo correction.

Researcher Actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended Queries

Research Summary

Contribution Summary

  • We evaluated seven open-source LLMs from three perspectives: (A) generation and information extraction of pathology diagnosis text following predefined formats, (B) correction of typographical errors in Japanese pathology reports, and (C)…
  • In contrast, preferences for explanatory outputs varied substantially across raters.

Why It Matters For Eval

  • We evaluated seven open-source LLMs from three perspectives: (A) generation and information extraction of pathology diagnosis text following predefined formats, (B) correction of typographical errors in Japanese pathology reports, and (C)…
  • In contrast, preferences for explanatory outputs varied substantially across raters.

Researcher Checklist

  • Pass: Human feedback protocol is explicit

    Detected: Pairwise Preference, Expert Verification

  • Gap: Evaluation mode is explicit

    No clear evaluation mode extracted.

  • Gap: Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Gap: Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Gap: Metric reporting is present

    No metric terms extracted.

Related Papers

Papers are ranked by protocol overlap, extraction signal alignment, and semantic proximity.

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