论文ICLR 2026 Poster2026 年trustworthy medical AI LiveClin：无泄漏的实时临床基准
ICLR 2026 Poster accepted paper at ICLR 2026. The reliability of medical LLM evaluation is critically undermined by data contamination and knowledge obsolescence, leading to inflated scores on static benchmarks. To address these challenges, we introduce LiveClin, a live benchmark designed for the approximating real-world clinical practice. Built from contemporary, peer-reviewed case reports and updated biannually, LiveClin ensures clinical currency and resists data contamination. Using a verified AI–human workflow involving 239 physicians, we transform authentic patient cases into complex, multimodal evaluation scenarios that span the entire clinical pathway. Code/project link: https://github.com/AQ-MedAI/LiveClin
论文ICLR 2026 Poster2026 年trustworthy medical AI Cancer-Myth：评估大语言模型回答含错误预设的患者问题
ICLR 2026 Poster accepted paper at ICLR 2026. Cancer patients are increasingly turning to large language models (LLMs) for medical information, making it critical to assess how well these models handle complex, personalized questions. However, current medical benchmarks focus on medical exams or consumer-searched questions and do not evaluate LLMs on real patient questions with patient details. In this paper, we first have three hematology-oncology physicians evaluate cancer-related questions drawn from real patients. While LLM responses are generally accurate, the models frequently fail to recognize or address false presuppositions} in the questions, posing risks to safe medical decision-making.
数据资源medical exam question-answer textmedical exam QA benchmarkUSMLE, Mainland China, and Taiwan exam-style QA splits; see repository开放访问 MedQA：含美国、中国大陆与台湾拆分的医学考试问答数据集
MedQA is a medical examination question answering benchmark with English and Chinese medical licensing-style question sets, including mainland China and Taiwan variants. It is widely used for medical QA and medical reasoning evaluation.
数据资源Chinese medical exam and QA textChinese medical LLM evaluation benchmarkMultiple Chinese medical exam and benchmark splits; see Hugging Face card开放访问 CMB：中文医学基准
CMB is a comprehensive Chinese medical benchmark for evaluating medical large language models on medical exams, reasoning, and clinical knowledge questions. It is suited for Chinese medical QA, LLM evaluation, and instruction-following assessment.
数据资源TextLLM evaluation benchmarkHealth AI evaluation benchmark开放访问 HealthBench 健康 AI 评测基准
Benchmark for evaluating health AI model safety, helpfulness, and clinical-relevance judgments with physician-reviewed rubrics.
征稿与合作EMNLP 2026截止 北京时间 2026-05-25会议征稿 EMNLP 2026 征稿
CCF-Deadlines lists EMNLP 2026 with papers due 2026-05-25 UTC-12 and conference dates 2026-10-24 to 2026-10-29 in Budapest. EMNLP is relevant to clinical NLP, biomedical language models, medical text mining, EHR note understanding, and safe medical LLM evaluation.