Latest AI and machine learning research in cultural competence for healthcare professionals.
Background: Diagnostic errors are a leading cause of preventable patient harm, often occurring during early clinical encounters where diagnostic uncertainty is maximal. Large language models (LLMs) have shown potential in medical reasoning, yet their ability to function as a diagnostic safety net, specifically by identifying and correcting human diagnostic errors, remains systematically unquantifi...
Labeling bias arises during data collection due to resource limitations or unconscious bias, leading to unequal label error rates across subgroups or misrepresentation of subgroup prevalence. Most fairness constraints assume training labels reflect the true distribution, rendering them ineffective when labeling bias is present; leaving a challenging question, that \textit{how can we detect such la...
Despite high semantic alignment, modern text-to-image (T2I) generative models still struggle to synthesize diverse images from a given prompt. This la...
Compositional generalization, the ability to reason about novel combinations of familiar concepts, is fundamental to human cognition and a critical ch...
Sodium azide mutagenesis offers a powerful approach to generate genetic diversity for rice improvement, yet comprehensive characterization of mutant p...
Ecological theory predicts that local species richness can influence biogeographic rates of speciation, extinction, and dispersal. For instance, incre...
Osteoporosis is a silent yet debilitating disease that often remains undetected until fractures occur. While early prediction is crucial, most studies...
Text-to-video generation is expensive, so only a few samples are typically produced per prompt. In this low-sample regime, maximizing the value of eac...
This study investigates the impact of regularization of latent spaces through truncation on the quality of generated test inputs for deep learning cla...
Time-series imputation benchmarks employ uniform random masking and shape-agnostic metrics (MSE, RMSE), implicitly weighting evaluation by regime prev...
Skin cancer is one of the most common cancers worldwide and early detection is critical for effective treatment. However, current AI diagnostic tools ...
Large Language Models (LLMs) are increasingly integrated into financial workflows, but evaluation practice has not kept up. Finance-specific biases ca...
Background: Large language models (LLMs) are increasingly piloted as chat interfaces for chart review and clinical decision support. Although leading ...
Brain maps (e.g. retinotopy, somatotopy) vary across individuals. This is thought to reflect underlying computational differences. However, artificial...
Vision-Language Models (VLMs) are known to inherit and amplify societal biases from their web-scale training data with Indian being particularly misre...
In machine learning, "ground truth" refers to the assumed correct labels used to train and evaluate models. However, the foundational "ground truth" p...
Machine learning (ML) methods for proteins and RNAs rely on multiple sequence alignments (MSAs) and related datasets such as experimental mutagenesis ...
Recent advances in large language models (LLMs) have enabled the development of multimodal medical AI. While models such as MedGemini achieve high acc...
Out-of-distribution (OOD) detection is critical for the safe deployment of machine learning systems. Existing post-hoc detectors typically rely on mod...
Text-to-image diffusion models achieve impressive generation quality but inherit and amplify training-data biases, skewing coverage of semantic attrib...