FD‑SCoPE is a language‑model framework that answers clinicians’ questions about systematic review evidence tables, exposing the underlying query, selected trials, and derivation rule for each answer. It handles both directly recorded attributes and derived attributes, achieving high accuracy on an oncology evidence table of 159 immune‑checkpoint inhibitor trials. After incorporating expert corrections, its performance on unseen questions improved from 77.9% to 84.9% F1.
arXiv:2609.27987v1 Announce Type: new Abstract: Large language models (LLMs) have made substantial progress on medical question-answering, yet effective medical dialogue also requires learning to ask...
arXiv:2607. 10275v1 Announce Type: new Abstract: Large language models achieve high scores on medical knowledge assessments, yet clinical reasoning requires actively deciding what to investigate under uncertainty.
LogiMed‑RoB is a new benchmark that tests large language models (LLMs) on hierarchical logical consistency in medical risk‑of‑bias assessments, using 860 randomized controlled trials and 14,820 queries based on Cochrane Risk of Bias 2.0 expert logic. The benchmark evaluates models across four dimensions—Atomic Consistency, Domain Consistency, Aggregation Consistency, and Evidential Faithfulness—revealing a catastrophic error‑compounding effect where high atomic accuracy does not translate to end‑to‑end consistency. Experiments on ten state‑of‑the‑art LLMs show that even top models can fail to deduce correct outcomes in a significant portion of cases, highlighting a gap between evidence retrieval and reasoning. whyItMatters":"The study shows that high outcome accuracy can mask critical reasoning flaws, emphasizing the need for white‑box logical verification before deploying LLMs in clinical settings."
The paper introduces LogiMed‑RoB, a benchmark that tests large language models (LLMs) on hierarchical logical consistency in medical risk‑of‑bias assessments, using 860 randomized controlled trials and 14,820 queries based on Cochrane RoB 2.0 expert logic. It evaluates models across four dimensions—Atomic Consistency, Domain Consistency, Aggregation Consistency, and Evidential Faithfulness—revealing a severe Error Compounding Effect where high atomic accuracy does not translate to end‑to‑end consistency. Experiments on ten state‑of‑the‑art LLMs show that even top performers can collapse to 45.13% overall consistency, with some models nearly failing entirely, and that many models struggle to deduce correct outcomes from retrieved evidence.
FDARxBench is an expert‑curated benchmark designed to evaluate document‑grounded question answering on FDA drug label documents, focusing on generic drug assessment. It features a multi‑stage pipeline that generates high‑quality QA examples covering factual, multi‑hop, and refusal tasks, and includes protocols for both open‑book and closed‑book reasoning. Experiments with various language models show significant gaps in factual grounding, long‑context retrieval, and safe refusal behavior, highlighting the challenge of regulatory‑grade label comprehension.
arXiv:2608. 16831v1 Announce Type: new Abstract: Generative pretraining established reusable task representations; later work on language-based task conditioning and in-context learning showed that a fixed model could adapt its behavior from instructions and demonstrations.
arXiv:2607. 02983v1 Announce Type: new Abstract: Recent reasoning-centric Large Language Models (LLMs) have made significant strides, yet they predominantly operate on a passive-inference pattern that assumes complete information.
arXiv:2608. 12138v1 Announce Type: cross Abstract: General-purpose large language models (LLMs) have recently been reported to match or exceed specialized clinical AI tools on medical benchmarks, but such comparisons draw on a narrow set of systems and on benchmarks developed largely in high-income settings.
The paper introduces a joint fact‑verification score that evaluates both answers and the evidence submitted with them. On the FEVEROUS dataset, replacing the DCUF evidence with UnifEE evidence improves the strict score by about 9.6 percentage points, while answer accuracy rises only 1.96 points. The study also shows that increasing context length for large language models yields modest evidence‑gain improvements, and that detailed answer‑evidence analyses uncover patterns missed by aggregate metrics.
arXiv:2608.28592v1 Announce Type: new Abstract: Large language models (LLMs) achieve high scores on medical knowledge examinations, yet real-world oncology is not a knowledge test--it is a sequence o...
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.