The paper introduces a paired benchmark to detect hindsight bias in clinical language models by comparing model responses to questions posed at a clinically relevant cutoff versus the full timeline. It uses 171 case reports (40 sepsis, 131 GLP‑1/diabetes) with both human‑annotated and LLM‑generated time‑series data, evaluating accuracy, hindsight trap rate, answer instability rate, and hindsight bias rate. Results show that exposing models to the full timeline consistently increases hindsight bias, while truncating the timeline mitigates bias without sacrificing accuracy.
arXiv:2606. 09030v1 Announce Type: cross Abstract: Clinical early warning systems built on electronic health records, in which clinical observations are recorded as irregularly sampled medical time series (ISMTS), must deliver both calibrated risk scores for patient triage and interpretable rationales that clinicians can verify.
Understanding the temporal progression of symptoms in clinical narratives is critical for disease monitoring, safety surveillance, and causality assessment. Clinical narratives, however, rarely provide explicit temporal anchors.
arXiv:2608. 12779v1 Announce Type: cross Abstract: Understanding the temporal progression of symptoms in clinical narratives is critical for disease monitoring, safety surveillance, and causality assessment.
The paper investigates how medical large language models (LLMs) may exhibit narrative anchoring bias when presented with the same clinical case in different patient voices. Using the NarrativeShield SDoH MedQA dataset, the authors evaluate three Qwen2.5 instruction‑tuned LLMs (1.5B, 3B, 7B) on 300 clinical cases, reporting metrics such as persona‑level accuracy, counterfactual consistency, correct consistency, and narrative sensitivity error. The 7B model achieves the highest accuracy (56.33 %) and correct consistency (40.33 %), yet narrative sensitivity errors remain substantial (31.67 %).
arXiv:2607. 13036v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for decision support in healthcare, but clinical evidence is often incomplete or evolving.
The study evaluates large language models (LLMs) on sequential emergency department triage, where acuity labels are predicted from progressively longer nurse‑patient conversations. Six LLMs were tested at five checkpoints on simulated and physician‑authored dialogues, showing a decline from moderate‑to‑substantial agreement on full records to only fair‑to‑moderate agreement at each checkpoint. The models consistently anchor on chief complaint exchanges and fail to integrate later evidence, yielding low agreement with clinicians (QWK 0.295 vs. 0.887‑0.929) and concentrating predictions on ESI‑2 and ESI‑3. whyItMatters":"The findings reveal that LLMs, despite strong offline performance, cannot reliably handle the sequential nature of real‑time triage, highlighting a critical gap for safe deployment in emergency settings."
MMTClinic is a new benchmark that tests large language models on complex reasoning and question‑answering tasks involving clinical time‑series data. It combines text, medical images, and multivariate physiological signals to create 30,000 QA pairs—including 15,000 multiple‑choice and 15,000 open‑ended questions—in five languages (English, Hindi, Bengali, Marathi, and Tamil). The benchmark covers mortality prediction, heart‑rate forecasting, and SOFA score estimation, and evaluates 13 state‑of‑the‑art LLMs across zero‑shot, few‑shot, and chain‑of‑thought settings, revealing significant performance gaps across tasks, languages, and modalities.
arXiv:2609.10092v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly act as research agents, yet their ability to track shifts in research attention is difficult to evaluate be...
arXiv:2608. 09080v1 Announce Type: cross Abstract: Large Language Models (LLMs) have achieved strong performance in medical question answering and clinical reasoning tasks.
arXiv:2607. 09880v1 Announce Type: cross Abstract: Clinical time series are central to patient monitoring, risk assessment, and clinical decision support.
CliniCIRCA is a modular large‑language‑model framework that reconstructs longitudinal mental‑health patient journeys from raw electronic health record narratives. It temporally classifies clinical events in unstructured discharge summaries without explicit timestamps, producing 15,891 tagged events from 52 summaries and correcting 629 errors to create verified gold‑standard timelines. The framework then generates temporally grounded summaries, compressing each source by 1.52×, and scales to produce 1,000 silver‑standard timelines for training, showing that instruction tuning improves event extraction, temporal tagging, and summarization across models.
arXiv:2607. 09322v1 Announce Type: new Abstract: In this work, we introduce LongMedBench, a real-world EHR-based benchmark for long-horizon clinical decision-making.