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HFEPX · Eval paper review

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

Masataka Kawai, Singo Sakashita, Shumpei Ishikawa, Shogo Watanabe +7 more

Published

Mar 12, 2026

Citations

0

Trust level

Low

Usefulness score

40/100 (Low)

Extraction confidence

45% (Low)

Derived from extracted protocol signals and abstract evidence.

Rater population

Domain Experts

Signals refreshed

Mar 12, 2026

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Tags

Expert Verification Medicine Pairwise Preference

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.

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

Best use

Background context only

Use if you need

Background context only.

What to verify

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

Main weakness

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

Human feedback signal
Detected
From extracted signals
Evaluation signal
Weak or implicit
Validate from full paper
Usefulness for eval research
40/100
Adjacent candidate

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

If you are doing eval pipeline work, start here

Human Eval Papers LLM-as-Judge Papers Pairwise Preference Papers Tool-Use Evaluation Search

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.

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."

Benchmarks and datasets

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

Reported metrics

No metric terms were extracted from the available abstract.

Human feedback details
Uses human feedback
Yes
Feedback types
Pairwise Preference, Expert Verification
Rater population
Domain Experts
Expertise required
Medicine
Evaluation details
Evaluation modes
None
Agentic eval
None
Quality controls
Not reported
Evidence quality
Low
Use this page as
Background context only

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

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

  • Human feedback protocol is explicit

    Detected: Pairwise Preference, Expert Verification

  • Evaluation mode is explicit

    No clear evaluation mode extracted.

  • Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Metric reporting is present

    No metric terms extracted.