arXiv:2610. 35430v1 Announce Type: new Abstract: Large language models are increasingly used to turn the feature attributions of multimodal prognostic models into narratives for clinicians, yet costly modalities such as amyloid PET and cerebrospinal fluid biomarkers are often missing, and whether these explanations stay faithful under missing evidence has not been tested.
arXiv:2608. 14552v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly evaluated and used in medicine, but clinical usefulness depends on answer accuracy and whether confidence tracks evidence quality and uncertainty.
arXiv:2607. 11656v1 Announce Type: cross Abstract: Accurate diagnostic classification and disease-severity prediction for Alzheimer's disease are hampered by the incompleteness and heterogeneity of real-world clinical data.
arXiv:2603. 28387v2 Announce Type: replace Abstract: Trustworthy clinical AI requires that performance gains reflect genuine evidence integration rather than surface-level artifacts.
The paper introduces an interventional protocol to assess how vision‑language models (VLMs) explain the impact of missing modalities on their predictions. By comparing the models’ self‑explanations with actual changes observed after restoring missing inputs, the study finds that VLMs routinely overstate the sufficiency of available evidence and underestimate the effect of adding back missing modalities. Across eight open‑weight VLMs and four tasks, the discrepancy between predicted and realized changes is substantial, revealing systematic mischaracterization of modality dependence.
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.
arXiv:2603.28387v3 Announce Type: replace-cross Abstract: Trustworthy clinical AI must use real evidence and avoid relying on surface-level artifacts. We evaluate 12 open-weight vision-language model...
arXiv:2606. 19509v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly applied to structured clinical data, yet whether they can recognize the limits of their own knowledge on such tasks remains unexplored.
arXiv:2608.28714v1 Announce Type: cross Abstract: Objective: Deep learning accelerates brain MRI four- to tenfold, but models can erase lesions or synthesize false tissue - failures pixel-averaged me...
CoPoE is a new multimodal fusion framework for Alzheimer’s disease diagnosis that maps diverse clinical, imaging, genomic, and biomarker data into a structured latent space divided into four biological and clinical axes: genetic Risk, molecular Pathology, Neurodegeneration, and clinical Stage. Each available modality contributes a diagonal Gaussian expert, and a masked Product-of-Experts architecture fuses only the observed modalities, avoiding synthetic imputation. In extensive experiments on the ADNI dataset, CoPoE outperforms standard missing-modality fusion baselines, achieving higher AUROC, better calibration metrics, and retaining pathology signals even when certain modalities are missing.
DementiaCare-Bench is a new video benchmark designed to evaluate video-language models (VLMs) on understanding behavioral and psychological symptoms of dementia (BPSD). It contains 56 caregiver training videos, 94 clips across nine BPSD categories, and 2023 questions that are grounded in transcript spans and labeled by their visual demand. The benchmark reveals that most VLMs perform well on questions answerable by language alone but struggle with those requiring ordered frames, and a lightweight LoRA fine‑tune (DemCare‑VLM) can improve video dependence.
arXiv:2610.18141v1 Announce Type: new Abstract: Omnimodal large language models (OLLMs) are susceptible to cross-modal hallucinations under audio-visual interference. We find that, for a substantial...
arXiv:2603. 09986v3 Announce Type: replace-cross Abstract: Hallucinations, the tendency for large language models to provide responses with factually incorrect and unsupported claims, is a serious problem within natural language processing for which we do not yet have an effective solution to mitigate against.