arXiv:2606. 23992v1 Announce Type: cross Abstract: Clinical value sets define the standardized terminology codes used in quality measurement, phenotyping, cohort construction, and clinical decision support.
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The paper identifies a new problem in clinical natural language processing called the clinical lost‑in‑the‑middle (CLitM) effect, where large language models perform poorly on information located near the center of long electronic health record (EHR) documents. Using the MedAlign dataset, the authors quantify a 21.9‑percentage‑point accuracy gap across 2,196 instruction‑response pairs and six models, showing that most critical facts lie in the CLitM trough. They propose Query‑Conditioned Clinical Suppression (QCCS), a lightweight context‑selection gate that outperforms traditional retrieval methods (BM25, dense retrieval, cross‑encoder reranking) on a held‑out set of 83 instructions, achieving up to 25.3% accuracy for middle‑position queries. whyItMatters":"The study demonstrates that standard retrieval strategies fail to reliably surface central clinical information, and that a query‑aligned selection mechanism can substantially improve model performance on critical EHR data."
The paper introduces a retrieval‑augmented multi‑agent framework that automatically generates instance‑specific evaluation rubrics for medical language models. By retrieving authoritative medical evidence, decomposing it into atomic facts, and combining these with user interaction constraints, the system produces fine‑grained criteria that outperform GPT‑4o on HealthBench and LLMEval‑Med. The generated rubrics also guide response refinement, improving medical LLM output quality by 9.2%.
arXiv:2604. 08849v2 Announce Type: replace-cross Abstract: Many important retrieval problems are not merely problems of semantic similarity, but problems of constraint satisfaction: a retrieved item should be topically relevant to a query and satisfy explicit requirements involving negation, temporal conditions, numeric thresholds, exceptions, ontological relations, and incomplete evidence.
arXiv:2607. 20453v1 Announce Type: cross Abstract: Large language models show promise for clinical prediction, but zero-shot performance on specialized tasks is limited by incomplete domain knowledge, especially for smaller locally deployable models.
PRISM (Population‑Retrieved Inference for Structured Multimodal Prognosis) is a multimodal language model that integrates structured measurements, physiological waveforms, longitudinal history, and population context for clinical prognosis. It uses per‑feature numerical tokenization, recency‑weighted aggregation of ECGs, and a dual‑modality population memory bank, retrieving mean‑pooled embeddings of similar encounters without including individual records in the prompt. On the 1,443‑task MDS‑ED benchmark, PRISM achieves high AUROCs across diagnosis, deterioration, ICU admission, and mortality, outperforming baselines and demonstrating the value of structured multimodal integration and supervised external adaptation.
This study benchmarks transformer models for Bangla medical named entity recognition (NER), comparing BanglaBERT, multilingual BERT (mBERT), XLM‑RoBERTa, and GPT‑4o mini under zero‑shot and few‑shot prompting. Across a full test set of 3,179 samples, fine‑tuned XLM‑RoBERTa achieves a new state‑of‑the‑art F1‑score of 0.5959, while BanglaBERT lags with 0.4937, suggesting that domain diversity outweighs language specificity. The analysis shows high performance on Medicine and Specialist entities (F1 > 0.83) but lower accuracy on Symptoms (F1 0.4367), and demonstrates that fine‑tuned transformers outperform prompt‑only approaches by a factor of 3.76.