Reproduce “MedRECT: A Bilingual Medical Reasoning Benchmark for Error Correction in Clinical Texts” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Naoto Iwase, Hiroki Okuyama, Junichiro Iwasawa
MedRECT evaluates whether clinical text contains errors, identifies erroneous sentences, and generates corrections in Japanese and English. It uses Qwen3-32B in thinking and no-think configurations, comparing error-detection F1 and sentence-extraction accuracy. Thinking scores higher on both benchmarks, including extraction gains of 24.5 percentage points and 10.3 points. Decoding differs, and the benchmark does not establish clinical safety.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.