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Cultural Competence

Latest AI and machine learning research in cultural competence for healthcare professionals.

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Showing 841-861 of 3,213 articles
When Fairness Metrics Disagree: Evaluating the Reliability of Demographic Fairness Assessment in Machine Learning

The evaluation of fairness in machine learning systems has become a central concern in high-stakes a...

CausalDisenSeg: A Causality-Guided Disentanglement Framework with Counterfactual Reasoning for Robust Brain Tumor Segmentation Under Missing Modalities

In clinical practice, the robustness of deep learning models for multimodal brain tumor segmentation...

Learning Class Difficulty in Imbalanced Histopathology Segmentation via Dynamic Focal Attention

Semantic segmentation of histopathology images under class imbalance is typically addressed through ...

What Are We Really Measuring? Rethinking Dataset Bias in Web-Scale Natural Image Collections via Unsupervised Semantic Clustering

In computer vision, a prevailing method for quantifying dataset bias is to train a model to distingu...

Who Gets Flagged? The Pluralistic Evaluation Gap in AI Content Watermarking

Watermarking is becoming the default mechanism for AI content authentication, with governance polici...

Rethinking Image-to-3D Generation with Sparse Queries: Efficiency, Capacity, and Input-View Bias

We present SparseGen, a novel framework for efficient image-to-3D generation, which exhibits low inp...

A case report on gendered biases in a Finnish healthcare AI assistant

In this study, we investigate gender bias in a Retrieval-Augmented Generation (RAG) based AI assista...

Combating Pattern and Content Bias: Adversarial Feature Learning for Generalized AI-Generated Image Detection

In recent years, the rapid development of generative artificial intelligence technology has signific...

T2I-BiasBench: A Multi-Metric Framework for Auditing Demographic and Cultural Bias in Text-to-Image Models

Text-to-image (T2I) generative models achieve impressive visual fidelity but inherit and amplify dem...

Seeing Through Touch: Tactile-Driven Visual Localization of Material Regions

We address the problem of tactile localization, where the goal is to identify image regions that sha...

Spatial Organellomics Maps Cell State Diversity and Metabolic Adaptation in Tissues

Cell state diversity drives tissue adaptability, repair, and disease resilience, but fully capturing...

Tarot-SAM3: Training-free SAM3 for Any Referring Expression Segmentation

Referring Expression Segmentation (RES) aims to segment image regions described by natural-language ...

CAFP: A Post-Processing Framework for Group Fairness via Counterfactual Model Averaging

Ensuring fairness in machine learning predictions is a critical challenge, especially when models ar...

Is CLIP Cross-Eyed? Revealing and Mitigating Center Bias in the CLIP Family

Recent research has shown that contrastive vision-language models such as CLIP often lack fine-grain...

On-the-fly Repulsion in the Contextual Space for Rich Diversity in Diffusion Transformers

Modern Text-to-Image (T2I) diffusion models have achieved remarkable semantic alignment, yet they of...

Diversity Matters: Dataset Diversification and Dual-Branch Network for Generalized AI-Generated Image Detection

The rapid proliferation of AI-generated images, powered by generative adversarial networks (GANs), d...

Diagnostic Accuracy of Large Language Models for Rare Diseases: A Systematic Review and Meta-Analysis

Background: Large language models (LLMs) have been evaluated as tools to assist rare disease diagnos...

Demographic Fairness in Multimodal LLMs: A Benchmark of Gender and Ethnicity Bias in Face Verification

Multimodal Large Language Models (MLLMs) have recently been explored as face verification systems th...

Latent Bias Alignment for High-Fidelity Diffusion Inversion in Real-World Image Reconstruction and Manipulation

Recent research has shown that text-to-image diffusion models are capable of generating high-quality...

DAK-UCB: Diversity-Aware Prompt Routing for LLMs and Generative Models

The expansion of generative AI and LLM services underscores the growing need for adaptive mechanisms...

Policy-based Tuning of Autoregressive Image Models with Instance- and Distribution-Level Rewards

Autoregressive (AR) models are highly effective for image generation, yet their standard maximum-lik...

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