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Cultural Competence

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

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Showing 820-840 of 3,213 articles
Verification Mirage: Mapping the Reliability Boundary of Self-Verification in Medical VQA

Self-verification, re-invoking the same vision language model (VLM) in a fresh context to check its ...

Structural bias in machine learning-guided peptide design

Machine learning continues to accelerate peptide and protein design through the rapid prediction and...

Towards Fairness under Label Bias in Image Segmentation: Impact, Measurement and Mitigation

Labeled datasets reflect the biases of their annotation pipelines, which sometimes introduce label b...

The Cost of Context: Mitigating Textual Bias in Multimodal Retrieval-Augmented Generation

While Multimodal Large Language Models (MLLMs) are increasingly integrated with Retrieval-Augmented ...

Correcting heterogeneous diagnostic bias when developing clinical prediction models using causal hidden Markov models

In routine care, individuals identified a priori as high-risk are usually tested for conditions more...

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance

We present EDDY (Exact-marginal Diversification via Divergence-free dYnamics), a guidance mechanism ...

BAMI: Training-Free Bias Mitigation in GUI Grounding

GUI grounding is a critical capability for enabling GUI agents to execute tasks such as clicking and...

Self-Care Competence and AI-Supported Learning as Predictors of Enhanced Clinical Decision-Making Skills Among Nurses

Clinical decision-making is a critical competency for nurses particularly in resource-constrained he...

Artificial Intelligence Agents in Mental Health: A Systematic Review and Meta Analysis

The rapid rise of large language models (LLMs) and foundation models has accelerated efforts to buil...

MIFair: A Mutual-Information Framework for Intersectionality and Multiclass Fairness

Fairness in machine learning remains challenging due to its ethical complexity, the absence of a uni...

Fidelity, Diversity, and Privacy: A Multi-Dimensional LLM Evaluation for Clinical Data Augmentation

The scarcity of high-quality annotated medical data, particularly in mental health, poses a signific...

Disrupted oral microbial networks and reproducible community signatures implicate the oral-gut axis in Crohn's disease

Background: Emerging evidence suggests that the oral microbiome may contribute to aberrant gut immun...

Correcting Performance Estimation Bias in Imbalanced Classification with Minority Subconcepts

Class-level evaluation can conceal substantial performance disparities across subconcepts within the...

Validation of an AI-Assisted Framework for Systematic Bias Assessment in Observational Studies

Background: The rapid expansion of medical literature has led to substantial variability and frequen...

Improving Diversity in Black-box Few-shot Knowledge Distillation

Knowledge distillation (KD) is a well-known technique to effectively compress a large network (teach...

Meta-Ensemble Learning with Diverse Data Splits for Improved Respiratory Sound Classification

Training reliable respiratory sound classification models remains challenging due to the limited siz...

FAIR_XAI: Improving Multimodal Foundation Model Fairness via Explainability for Wellbeing Assessment

In recent years, the integration of multimodal machine learning in wellbeing assessment has offered ...

ReTrack: Evidence-Driven Dual-Stream Directional Anchor Calibration Network for Composed Video Retrieval

With the rapid growth of video data, Composed Video Retrieval (CVR) has emerged as a novel paradigm ...

Soft Label Pruning and Quantization for Large-Scale Dataset Distillation

Large-scale dataset distillation requires storing auxiliary soft labels that can be 30-40x larger on...

MM-JudgeBias: A Benchmark for Evaluating Compositional Biases in MLLM-as-a-Judge

Multimodal Large Language Models (MLLMs) have been increasingly used as automatic evaluators-a parad...

Embedding Arithmetic: A Lightweight, Tuning-Free Framework for Post-hoc Bias Mitigation in Text-to-Image Models

Modern text-to-image (T2I) models amplify harmful societal biases, challenging their ethical deploym...

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