Performance Evaluation of Open-Source Large Language Models for Assisting Pathology Report Writing in Japanese
arXiv:2603.11597v1 Announce Type: new 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.
arXiv:2603.11597v1 Announce Type: new 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.
Executive Summary
This study evaluates the applicability of seven open-source large language models (LLMs) in assisting pathology report writing in Japanese across three domains: structured diagnosis generation, typographical error correction, and subjective evaluation of explanatory outputs. The findings indicate that specialized and thinking-based LLMs outperform general models in tasks requiring logical reasoning and typo correction, while the subjective evaluation of explanatory text reveals significant rater variability. Importantly, the study demonstrates that open-source LLMs, though heterogeneous in performance, hold practical value in supporting specific, clinically relevant aspects of pathology documentation in Japan. The results suggest a targeted approach to LLM deployment—leveraging strengths in specific use cases rather than assuming uniform applicability across all tasks.
Key Points
- ▸ Open-source LLMs show differential effectiveness depending on task type (generation vs. correction vs. explanation)
- ▸ Specialized and thinking models excel in structured reporting and typo correction
- ▸ Subjective evaluator variability complicates generalized conclusions
Merits
Task-Specific Utility
Identification of task-specific strengths among LLMs allows for more efficacious deployment in clinical settings
Cost-Effectiveness
Open-source models offer accessible alternatives to proprietary systems without compromising clinical utility in targeted applications
Demerits
Generalization Limitation
Performance variance across rater groups for explanatory outputs undermines the ability to standardize model selection across institutional settings
Expert Commentary
The study represents a meaningful step toward contextualizing AI assistance within specialized medical domains. While the broad adoption of LLMs in pathology has been met with skepticism due to concerns over accuracy and interpretability, this work provides nuanced evidence that open-source models can offer tangible benefits when aligned with specific functional requirements. The distinction between generalist and specialized LLMs is particularly compelling—it validates the emerging trend of domain-specific model selection as a pragmatic and effective strategy. Moreover, the subjective evaluation component, though variable, underscores an important reality: human-AI interaction in clinical settings is inherently interpretive. Therefore, future research should incorporate more robust rater calibration protocols or machine learning-based consensus modeling to mitigate variability in subjective assessments. Importantly, the findings also have broader implications beyond pathology—they suggest a paradigm shift in AI-assisted clinical documentation: from uniform, generalized models toward a modular, task-optimized ecosystem. This shift could reduce overhead, improve usability, and enhance adoption rates in under-resourced clinical environments globally.
Recommendations
- ✓ 1. Clinicians and administrators should evaluate LLMs based on specific use-case alignment—select thinking models for diagnostic generation, specialized models for typo correction, and consider subjective evaluation feedback as supplementary rather than definitive.
- ✓ 2. Academic institutions and regulatory bodies should develop task-specific validation frameworks for AI tools in medical documentation, recognizing that generalizability is limited and that contextual fit matters more than overall performance metrics.