The paper introduces a benchmark of over 6,000 clinical triage scenarios, 7,000 physician annotations, and 225,000 large language model (LLM) responses to assess how LLMs perform under realistic variations in clinical text. The study finds that LLMs tend to recommend unnecessary care more often than physicians, especially when the input text is perturbed, and that LLM recommendations are more sensitive to gender and tone changes than human recommendations. These findings underscore the importance of deployment‑oriented evaluations that reflect expert physician behavior.
arXiv:2604.26766v2 Announce Type: replace-cross Abstract: Accurate and consistent Emergency Severity Index (ESI) assignment remains a persistent challenge in emergency departments, where highly varia...
arXiv:2609.14819v1 Announce Type: cross Abstract: Large language models (LLMs) have a growing range of applications in medicine, and their evaluation is critical for ensuring they provide benefit and...
arXiv:2412.15957v2 Announce Type: replace-cross Abstract: The rapid development of large language models (LLMs) has transformed many industries, including healthcare. In practice, hospitals and patie...
arXiv:2512. 01241v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are routinely used by physicians and patients for medical advice, yet their clinical safety profiles remain poorly characterized.
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)....
arXiv:2606. 07951v1 Announce Type: cross Abstract: Humans increasingly turn to Language Models (LMs) in ways that shape beliefs and drive decisions, including discussing, rewriting, and summarizing information from scientific articles, news, and medical reports.
arXiv:2606. 00019v1 Announce Type: cross Abstract: Ambient artificial intelligence (AI) documentation tools are increasingly deployed to reduce clinician documentation burden, but their implications for biased language in clinical notes remain unclear.
MIRA is a bilingual benchmark that evaluates whether large language models (LLMs) provide consistent medical information across different user phrasings, languages, and health literacy levels. It contains 4,320 prompts derived from 60 medically reviewed low‑risk health questions and reveals that models tend to omit key information and offer fewer concrete next steps when responding to low health‑literacy signals, a phenomenon termed Differential Information Dilution (DID). A knowledge‑guided mitigation prompt can reduce this dilution for most models, notably improving Claude and Qwen.
arXiv:2608.30022v1 Announce Type: new Abstract: Introduction: NICE guidelines provide evidence-based recommendations for clinical care but remain largely in unstructured natural language. Existing ap...
The paper introduces MEGA-CDP, a benchmark designed to evaluate medical large language models (LLMs) on their ability to generate clinical decision pathways (CDPs) that adhere to clinical practice guidelines. MEGA-CDP is built from 2,274 English and Chinese guidelines, producing 42,353 clinical cases with explicit reference CDPs, and supports both single-turn and multi-turn interactions. Experiments on 16 LLMs reveal that reliable guideline adherence remains difficult, underscoring the need for CDP-focused evaluation and the potential of MEGA-CDP to advance medical LLM performance.
arXiv:2608. 10273v1 Announce Type: cross Abstract: Deploying large language models (LLMs) for decision support in emergency departments (EDs) faces two major challenges: privacy risks of transmitting patient data to closed-source commercial LLMs and the lack of systematic evaluation of fine-tuning strategies for locally deployable open-source small language models (SLMs).
arXiv:2606. 07237v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used in healthcare for tasks such as clinical question answering, diagnosis support, and report summarization.