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.
By Maroof Kousar, Yibo Hu
arXiv:2608. 03028v1 Announce Type: new Abstract: Applying a valid medication-safety rule when its patient-specific conditions are not met can produce an incorrect decision.
By Zhitian Hou, Yuhang Liu, Pengkai Wang, Zeyu Liu, Guanghao Zhu, Zheng Liu, Shuo Cai, Congkai Xie, Zhijie Sang, Kun Zeng, Hongxia Yang
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.
By Betty Xiong, Jillian Fisher, Benjamin Newman, Meng Hu, Shivangi Gupta, Yejin Choi, Lanyan Fang, Russ B Altman
The paper introduces HarmReduction, a benchmark for evaluating large language models (LLMs) on their ability to provide accurate and safe harm reduction information to people who use drugs (PWUD). The benchmark, HR-Basic, contains 2,160 question‑answer‑evidence pairs covering safety boundary checks, quantitative value provision, and polysubstance risk inference. Experiments show that even state‑of‑the‑art LLMs struggle with accuracy and can pose severe safety risks, underscoring the need for a dedicated evaluation framework.
By Kaixuan Wang, Chenxin Diao, Jason T. Jacques, Zhongliang Guo, Shuai Zhao
The paper introduces a framework to predict whether a compound’s potency can be quantified in dose‑response profiling, treating quantifiability as a separate triage goal from biological activity. It shows that features from low‑cost primary screens, rather than molecular structure, strongly predict quantifiability, and that this prediction holds across new chemical scaffolds and assay families. The authors argue that incorporating quantifiability predictions can better allocate expensive dose‑response resources.
By Sean Lim
arXiv:2611.00460v1 Announce Type: cross
Abstract: Large language models (LLMs) now answer medical exam questions at or above expert level, but it is unclear whether such scores reflect clinical compe...
By Navapat Nananukul, Mayank Kejriwal