arXiv:2609.14825v1 Announce Type: cross Abstract: Large language models (LLMs) are often deemed unsafe for clinical question answering because of their tendency to hallucinate. Retrieval augmentation...
arXiv:2604. 04593v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) grounds large language models in external medical knowledge, yet standard retrievers frequently surface hard negatives that are semantically close to the query but describe clinically distinct conditions.
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: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:2608. 16643v1 Announce Type: cross Abstract: Automated detection of errors in clinical documentation is a promising application of large language models (LLMs), yet decisions to deploy such models rest on benchmarks that evaluate each clinical note in isolation.
MedFabric is a new benchmark for detecting word‑level medical fabrications, comprising 646 fabricated statements each paired with a ground‑truth passage that shares the same LLM authorship and nearly identical wording. The study shows that current detectors perform poorly—expert clinicians achieve only 53.3% macro‑F1 and no detector family surpasses 60% without gold evidence—highlighting that detection hinges on evidence correctness rather than subtlety of fabrication. The authors demonstrate that a retrieval‑confidence gate can substantially improve performance, raising macro‑F1 from 61% to 74%.
arXiv:2607. 04223v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) reduces but does not eliminate hallucination, and existing detectors return a single answer-level score that does not indicate which sentence is unsupported, or why.
arXiv:2604. 27720v2 Announce Type: replace Abstract: Vision-language models (VLMs) are increasingly applied to medical visual question answering (Med-VQA), yet whether they can \emph{localize} the evidence behind their answers---a prerequisite for clinical auditability---is poorly characterized.
Automated detection of errors in clinical documentation is a promising application of large language models (LLMs), yet decisions to deploy such models rest on benchmarks that evaluate each clinical note in isolation. Error-detection benchmarks are typically constructed by injecting errors into notes, such that each erroneous note has a natural counterpart.
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:2606. 24200v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) in clinical settings increasingly requires multilingual retrieval against predominantly English evidence corpora.
arXiv:2609.12884v1 Announce Type: new Abstract: A medical claim's correctness often depends not on the claim alone, but on the clinical structure around it. A claim may require a lab reference range,...