arXiv AI
MedRECT: A Bilingual Medical Reasoning Benchmark for Error Correction in Clinical Texts
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Aug 18
Toward Better Assessment of LLMs' Performance in Clinical Error Detection
arXiv:2608. 16643v1 Announce Type: cross Abstract: Automated detection of errors in clinical documentation is a promising application of large language models (LLMs), yet decisions to deploy such models rest on benchmarks that evaluate each clinical note in isolation.
arXiv AI
Jun 9
Beyond English benchmarks: clinical llm evaluation in Brazilian Portuguese
arXiv:2606. 07853v1 Announce Type: cross Abstract: Large Language Models are transforming the support for clinical decision and their application in real scenarios.
Hugging Face Trending Papers
Aug 17
Toward Better Assessment of LLMs' Performance in Clinical Error Detection
Automated detection of errors in clinical documentation is a promising application of large language models (LLMs), yet decisions to deploy such models rest on benchmarks that evaluate each clinical note in isolation. Error-detection benchmarks are typically constructed by injecting errors into notes, such that each erroneous note has a natural counterpart.
arXiv Computation and Language
Aug 21
HealMed: Multilingual Evaluation of Large Language Models in Medicine
arXiv:2608. 19981v1 Announce Type: new Abstract: We present HealMed, an expert-reviewed benchmark for multilingual evaluation of large language models in medicine.
arXiv AI
Jun 24
MMed-Bench-IR: A Heterogeneous Benchmark for Multilingual Medical Information Retrieval
arXiv:2606. 24200v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) in clinical settings increasingly requires multilingual retrieval against predominantly English evidence corpora.
arXiv AI
1d ago
Scaling Clinical Judgment to Evaluate Medical AI
arXiv:2609.12822v2 Announce Type: replace Abstract: Blinded physician evaluation has been considered by many to be the gold standard for assessing clinical reasoning in large language models (LLMs)....