多篇研究论文正在探索大型语言模型（LLMs）在临床环境中的评估与应用。其中一篇论文介绍了 STEP-CTS，一种从临床时间序列中为 LLM 预测选择可追溯证据的方法，其表现优于现有的基于文本的基线。另一篇论文回顾了用于评估 LLM 临床推理的现有评分标准，指出了在时间综合和忠实度等方面的差距。此外，研究还在调查临床分诊中开源 LLM 的偏见，开发用于医学图像推理的多模态 LLM，并创建 KlinikeBench 等基准来评估 LLM 的能力，而不仅仅是简单的诊断准确性。LLM 在临床医学中的更广泛应用，包括其可靠性和安全性，也是一个日益关注的领域。 AI
影响 这些研究强调了在医疗保健领域安全有效地部署 LLM，需要更强大的评估框架和专用模型的日益增长的需求。
排序理由 多篇 arXiv 论文介绍了临床环境中 LLM 的新基准、方法和分析。
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arXiv:2605.20292v2 Announce Type: replace Abstract: Numerical time-series models effectively process irregular electronic health record (EHR) trajectories, but do not expose which temporal patterns support each prediction as readable evidence. Existing text-based interfaces eithe…
arXiv:2610.01938v1 Announce Type: cross Abstract: Exam-style accuracy does not establish whether large language models (LLMs) reason well over clinical records. We define clinical reasoning as integrating and updating evidence across time and sources to form, revise and justify a…
arXiv:2610.01963v1 Announce Type: new Abstract: Emergency department (ED) triage is a high-stakes prioritization task in which demographic, socioeconomic, and system-context information may improperly influence acuity assignment. Although open-source large language models (LLMs) …
arXiv:2511.22232v2 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) are increasingly capable in medical imaging, yet most focus on single-image settings. Clinical interpretation often requires integrating evidence across multiple images, such as dif…
arXiv:2609.38480v1 Announce Type: cross Abstract: Most clinical benchmarks evaluate language models (LMs) on diagnosis using complete case descriptions. In clinical practice, however, patients present information in different ways, and clinicians must obtain relevant history and …
arXiv:2511.00421v2 Announce Type: replace-cross Abstract: 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 be…
arXiv:2609.37788v1 Announce Type: cross Abstract: Rubrics support the structured evaluation of language models. We propose a rubric for assessing expressed clinical reasoning in model responses, drawing on three bodies of work: medical education assessment frameworks (ART, SCT, K…
arXiv:2609.34780v2 Announce Type: replace Abstract: The use and applicability of artificial intelligence (AI) in medical research and clinical practice has received increasing attention in the literature over recent years. The emergence of large language models (LLMs) has expande…