Performance Evaluation of Open-Source Large Language Models for Assisting Pathology Report Writing in Japanese
This work addresses the need for AI support in Japanese pathology report writing, though it is incremental as it applies existing models to a new domain.
The study evaluated seven open-source large language models for assisting pathology report writing in Japanese, finding that thinking and medical-specialized models performed better in structured reporting and typo correction tasks, while preferences for explanatory outputs varied among raters.
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.