Latest AI and machine learning research in cultural competence for healthcare professionals.
Large Language Models (LLMs) are transforming clinical practice and research, but their adoption requires rigorous evaluation. While human assessment is ideal, its cost has driven the widespread use of LLMs as evaluators. We introduce an open-source reciprocal framework comparing 71 human experts against six LLMs. AI evaluators show a strong self-preference bias, yet neither group reliably identif...
Despite their remarkable performance, Vision Language Models (VLMs) incur substantial computational overhead due to the large number of visual tokens. While diversity maximization has become a dominant strategy for token reduction, existing methods rely on cosine-based normalized similarity that discards magnitude information, failing to faithfully approximate the original feature representation a...
Recent progress has shown promise in distilling multi-step video diffusion models into efficient few-step students. Among them, Distribution Matching ...
Reject inference methods are widely used to mitigate survival bias in credit scoring, yet their effectiveness remains poorly understood. We systematic...
Five years after the discovery of persistent anti-Muslim bias in large language models, most evaluations remain confined to single-turn prompt complet...
As a cornerstone of the central dogma, RNA has both witnessed and actively shaped three billion years of evolution. Over this vast timescale, a remark...
Emotionally ambiguous facial expressions provide a tractable model for studying how uncertain affective information is resolved into categorical judgm...
The proliferation of recursive training on synthetic data can alleviate data scarcity but risks model collapse, where repeated training erodes distrib...
Antibodies are powerful therapeutics whose antigen specificity arises from sequence diversity shaped during development. Recently, language models tra...
Cultural garments pose a unique challenge for visual retrieval systems, as their identity often depends on subtle structural and symbolic details that...
Editing pretrained neural networks requires specialized algorithms tailored to specific objectives. Designing such algorithms is often time-consuming ...
Background: Digital decision-support tools such as triage systems and symptom checkers support millions of health-related decisions each year. Their q...
Adversarial examples reveal vulnerabilities in Vision-Language Pre-training (VLP) models and provide insights for improving robustness. A key property...
Lipschitz-style individual fairness formalizes the idea that semantically similar examples should receive similar predictions, but its evaluation in m...
In recent years, unified multimodal models (UMMs) have emerged to support both understanding and generation within a single framework. Mastering dynam...
Recent text-to-image models built on large-scale Transformer backbones and flow-based objectives deliver strong text-image alignment and high visual q...
Plant ecological and evolutionary strategies are shaped by interactions between phylogenetic history and environmental constraints, resulting in leaf ...
Background. Fairness-aware machine learning increasingly targets demographic performance disparities in clinical prediction, yet whether standard bias...
In real-world applications, models are expected to perform reliably across diverse settings. Yet, many existing multimodal benchmarks expand task type...
As large language models (LLMs) enter clinical workflows, automation bias, the uncritical acceptance of automated output, poses a patient-safety risk....