MedRECT: A Medical Reasoning Benchmark for Error Correction in Clinical Texts

Large language models (LLMs) show promise in medical applications, but their ability to detect and correct errors in clinical texts remains under-evaluated, particularly beyond English. We introduce MedRECT, a bilingual benchmark for Japanese and English that formulates medical error handling as three subtasks: error detection, error sentence extraction, and error correction. MedRECT-ja contains 663 samples derived from the Japanese Medical Licensing Examinations, while the separately sourced MedRECT-en contains 458 samples curated from MEDEC. We evaluate 11 LLMs across 17 configurations that cover proprietary and open-weight models, medical-domain specialization, and multiple reasoning settings. Qwen3-32B scores higher in its thinking mode than in its non-thinking mode on error detection F1 and sentence extraction accuracy in both subsets, with sentence extraction accuracy higher by 24.5 percentage points on MedRECT-ja and 10.3 on MedRECT-en. Several leading general-purpose reasoning models outperform all three evaluated medical-domain models on these two subtasks. Most models have lower point estimates on the Japanese subset, although absolute scores are not directly comparable because the subsets differ in source material and error distributions. LoRA fine-tuning yields higher sentence extraction accuracy and higher point estimates on all three reference-based correction similarity metrics in both languages. MedRECT provides an open, reusable evaluation resource for studying medical error correction and reasoning across Japanese and English. Our dataset and code are available at https://github.com/pfnet-research/medrect.