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:2607. 06452v1 Announce Type: cross Abstract: Biomedical question answering requires not only accurate extraction of information from scientific literature but also reliable integration of evidence across multiple documents.
DoseBench is a new benchmark consisting of 230 consumer‑style questions about whether a person can safely take another dose of acetaminophen or ibuprofen. The study evaluates eight large language models, finding that overall accuracy ranges from 44.8 % to 62.2 %, but models with similar accuracy can make vastly different dosing decisions, with some incorrectly approving up to 60.5 % of prohibited doses. The authors show that providing more guidance or drug facts can improve accuracy but may also cause models to overlook missing information, underscoring the difficulty of applying safety rules correctly in health QA.
MedRAGChecker is a claim-level verification framework designed for biomedical retrieval‑augmented generation (RAG). It decomposes generated answers into atomic claims and assesses each claim’s support by combining evidence‑grounded natural language inference with biomedical knowledge‑graph consistency signals. The aggregated claim decisions provide diagnostics that distinguish retrieval and generation failures, such as faithfulness, under‑evidence, contradiction, and safety‑critical errors, and the system is distilled into compact models for scalable evaluation.
arXiv:2601.03471v4 Announce Type: replace-cross Abstract: Reliable epidemiological reasoning requires synthesizing study evidence to infer disease burden, transmission dynamics, and intervention effe...
arXiv:2607. 19678v1 Announce Type: cross Abstract: AI-generated answers in high-stakes domains are often fluent but difficult to verify, especially when they contain multi-step reasoning rather than a single final answer.
arXiv:2606. 04262v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for everyday health questions, including whether a user can safely take another dose of an over-the-counter (OTC) medication.
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:2609.15964v1 Announce Type: new Abstract: Large language models (LLMs) have been widely adopted for clinical question answering (QA). Current systems can attach citations to their answers, but...
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.
arXiv:2607. 12310v1 Announce Type: cross Abstract: While modern question answering (QA) systems excel on clean, schema-aligned corpora, real-world knowledge is rarely so neatly packaged.
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.
The paper introduces BRIE, a scalable framework that automatically creates question–answer pairs from longitudinal electronic health record notes, validated by nineteen clinicians. It offers a continuously maintainable benchmark for evaluating large language models in clinical settings, addressing limitations of manual, costly, and quickly outdated existing benchmarks. Experiments across nine LLMs and five inference strategies reveal that even state‑of‑the‑art systems often miss clinically important information, especially for synthesis‑heavy queries.