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IndoNutriQA: An Indonesian Clinical Nutrition Question Answering Benchmark
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arXiv Computation and Language
1d ago
IndoNutriQA: An Indonesian Clinical Nutrition Question Answering Benchmark
IndoNutriQA is a newly released Indonesian clinical nutrition question‑answer benchmark comprising 911 expert‑curated question‑answer pairs drawn from real clinical‑nutrition reports. Each pair is grounded in a clinically relevant context and enriched with structured metadata such as anthropometric assessment, medical diagnosis, and document provenance. The dataset supports generative, extractive, and retrieval‑augmented QA, and includes fine‑tuned baselines and zero‑shot evaluations of contemporary large language models.
arXiv AI
Jun 16
EHRNote-ChatQA: A Benchmark for Evidence-Grounded Multi-Turn Clinical Question Answering over Longitudinal Discharge Summaries
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 Computation and Language
Aug 31
UIC-AIHealth4All at ArchEHR-QA 2026: Answer-First Evidence Grounding for Clinical Question Answering
The UIC-AIHealth4All system was presented for the ArchEHR-QA 2026 shared task on grounded question answering from electronic health records. It participated in evidence identification, answer generation, and answer‑evidence alignment, using an answer‑first pipeline that generates candidate answers with cited note sentences before classifying the full evidence set. The system ranked third in evidence identification, ninth in answer generation, and fifth in answer‑evidence alignment, and a linguistic analysis showed its outputs were harder to read than clinician‑authored references, highlighting the need for readability optimization in clinical NLP.
arXiv AI
Aug 13
A corpus-specific clinical RAG system matches or outperforms newer frontier LLMs on HealthBench
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.
arXiv AI
Jul 9
Healthier LLMs: Retrieval-Augmented Generation for Public Health Question Answering
arXiv:2607. 06641v1 Announce Type: cross Abstract: Large language models (LLMs) achieve promising results on medical question answering benchmarks, yet their use in public health is constrained by hallucinations and the rapid evolution of official guidance.
arXiv AI
Sep 7
PetQA: Benchmarking Veterinary Knowledge and Clinical Reasoning
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.