arXiv:2606. 15735v1 Announce Type: cross Abstract: Discharge summaries are crucial clinical documents containing the context of a patient's overall hospital stay, and are routinely reviewed by medical experts for patient readmission, ongoing care, and diagnostic decision-making.
arXiv:2509. 02594v3 Announce Type: replace-cross Abstract: Evaluating large language models (LLMs) on their ability to generate high-quality, accurate, situationally aware answers to clinical questions requires going beyond conventional benchmarks to assess how these systems behave in complex, high-stakes clinical scenarios.
The paper introduces a retrieval‑augmented multi‑agent framework that automatically generates instance‑specific evaluation rubrics for medical language models. By retrieving authoritative medical evidence, decomposing it into atomic facts, and combining these with user interaction constraints, the system produces fine‑grained criteria that outperform GPT‑4o on HealthBench and LLMEval‑Med. The generated rubrics also guide response refinement, improving medical LLM output quality by 9.2%.
arXiv:2606. 17474v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly considered for use in clinical consultation tasks, yet most medical evaluations remain static, single-turn, or narrowly outcome-based, limiting their ability to reflect the sequential, uncertain, and interactive nature of real-world care.
arXiv:2610.08093v1 Announce Type: cross Abstract: Developing reliable models for clinical tasks, such as Medical Question Answering (QA), is severely constrained by the limited availability of high-q...
arXiv:2409. 07314v3 Announce Type: replace-cross Abstract: While Large Language Models (LLMs) achieve superhuman performance on standardized medical licensing exams, these static benchmarks have become saturated and increasingly disconnected from the functional requirements of clinical workflows.
The study evaluates small recurrent‑depth language models for multitask clinical text processing, fine‑tuning five models on nine datasets and testing on four unseen datasets. Across 13 tasks, Ouro‑1.4B‑Thinking outperformed Qwen3‑1.7B on 11 datasets, while Ouro‑2.6B‑Thinking matched Qwen3‑8B on most tasks, with the largest gains seen in inference and question answering. The results suggest recurrent‑depth architectures merit further controlled investigation for clinical NLP.
PetQA is a Korean long‑form question‑answering benchmark designed to assess veterinary knowledge and clinical reasoning in large language and vision‑language models. It comprises 10,076 text‑only and 8,751 multimodal QA pairs about dogs and cats, with expert veterinarian answers, and a test split called PetQA‑Bench that includes question type and clinical condition annotations. The study evaluates 18 models across zero‑shot, retrieval‑augmented generation, and supervised fine‑tuning settings using ROUGE, BERTScore, and LLM‑as‑a‑judge metrics, revealing current models’ strengths and limitations and underscoring the need for better adaptation methods for clinically reliable veterinary AI; translated versions in five languages are also provided.
The paper introduces AI Consilium, a benchmark for evaluating large language models (LLMs) on providing clinically grounded, coherent, actionable, and safe second opinions in multidisciplinary settings. Using 300 NEJM–MedQA cases, the study compares eight single-model pipelines that generate role-specific opinions and interactive-consilium reports under free-form and structured conditions. Results show that structured final reports generally score higher, especially in management, and that GPT‑5.6‑Sol outperforms other models across all evaluated components, demonstrating the benchmark’s ability to differentiate model capabilities and the impact of response structure.
The paper introduces BRIE, a continuously maintainable benchmark for evaluating large language models (LLMs) in electronic health record (EHR) information retrieval. It presents a scalable framework that automatically generates question–answer pairs from longitudinal EHR notes, validated by nineteen clinicians. The benchmark allows assessment of multiple inference strategies and highlights that state‑of‑the‑art LLMs often miss clinically important information, especially when synthesis across documents is required.
The paper identifies a new problem in clinical natural language processing called the clinical lost‑in‑the‑middle (CLitM) effect, where large language models perform poorly on information located near the center of long electronic health record (EHR) documents. Using the MedAlign dataset, the authors quantify a 21.9‑percentage‑point accuracy gap across 2,196 instruction‑response pairs and six models, showing that most critical facts lie in the CLitM trough. They propose Query‑Conditioned Clinical Suppression (QCCS), a lightweight context‑selection gate that outperforms traditional retrieval methods (BM25, dense retrieval, cross‑encoder reranking) on a held‑out set of 83 instructions, achieving up to 25.3% accuracy for middle‑position queries. whyItMatters":"The study demonstrates that standard retrieval strategies fail to reliably surface central clinical information, and that a query‑aligned selection mechanism can substantially improve model performance on critical EHR data."
arXiv:2607. 24838v1 Announce Type: cross Abstract: In medical multiple-choice question answering (MCQA), Retrieval-Augmented Generation (RAG) can supplement the domain knowledge of language models (LMs).