Performance Evaluation of Open-Source Large Language Models for Assisting Pathology Report Writing in Japanese
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arXiv
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| Autori principali: | , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866918384206086144 |
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| author | Kawai, Masataka Sakashita, Singo Ishikawa, Shumpei Watanabe, Shogo Matsuoka, Anna Sakurai, Mikio Fujimoto, Yasuto Takahara, Yoshiyuki Ohara, Atsushi Miyake, Hirohiko Ishii, Genichiro |
| author_facet | Kawai, Masataka Sakashita, Singo Ishikawa, Shumpei Watanabe, Shogo Matsuoka, Anna Sakurai, Mikio Fujimoto, Yasuto Takahara, Yoshiyuki Ohara, Atsushi Miyake, Hirohiko Ishii, Genichiro |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_11597 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Performance Evaluation of Open-Source Large Language Models for Assisting Pathology Report Writing in Japanese Kawai, Masataka Sakashita, Singo Ishikawa, Shumpei Watanabe, Shogo Matsuoka, Anna Sakurai, Mikio Fujimoto, Yasuto Takahara, Yoshiyuki Ohara, Atsushi Miyake, Hirohiko Ishii, Genichiro Computation and Language Artificial Intelligence 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. |
| title | Performance Evaluation of Open-Source Large Language Models for Assisting Pathology Report Writing in Japanese |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2603.11597 |