Cultural Competence

The role of artificial intelligence in maternal and child health: Progress, controversies, and future directions.

This debate paper examines the transformative potential of Artificial Intelligence (AI), specificall...

Extensive novel diversity and phenotypic associations in the dromedary camel microbiome are revealed through deep metagenomics and machine learning.

The dromedary camel, also known as one-humped camel or Arabian camel, is iconic and economically imp...

AI-generated dermatologic images show deficient skin tone diversity and poor diagnostic accuracy: An experimental study.

BACKGROUND: Generative AI models are increasingly used in dermatology, yet biases in training datase...

Leveraging computational linguistics and machine learning for detection of ultra-high risk of mental health disorders in youths.

Mental illnesses often manifest through behavioral changes, with speech serving as a key medium for ...

A high-throughput screening method for selecting feature SNPs to evaluate breed diversity and infer ancestry.

As the scale of deep whole-genome sequencing (WGS) data has grown exponentially, hundreds of million...

Integrating artificial intelligence in community-based diabetes care programmes: enhancing inclusiveness, diversity, equity and accessibility a realist review protocol.

INTRODUCTION: Marginalised populations-such as racialised groups, low-income individuals, newcomers ...

One Neuron Saved is One Neuron Earned: On Parametric Efficiency of Quadratic Networks.

Inspired by neuronal diversity in the biological neural system, a plethora of studies proposed to de...

The apportionment of dietary diversity in wildlife.

Evaluating species' roles in food webs is critical for advancing ecological theories on competition,...

Quantitative phase imaging with temporal kinetics predicts hematopoietic stem cell diversity.

Innovative identification technologies for hematopoietic stem cells (HSCs) have expanded the scope o...

Integrating equity, diversity, and inclusion throughout the lifecycle of artificial intelligence for healthcare: a scoping review.

The lack of Equity, Diversity, and Inclusion (EDI) principles in the lifecycle of Artificial Intelli...

Neural correlates of evaluative bias against AI-labeled versus human-labeled artworks.

The rapid advancement of generative artificial intelligence (AI) has enabled machines to produce cre...

Advancing fungal phylogenetics: integrating modern sequencing, dark taxa discovery, and machine learning.

The study of fungal genetics has undergone transformative advancements in recent decades, profoundly...

Transformative potential of artificial intelligence in US CDC HIV interventions: balancing innovation with health privacy.

Artificial intelligence (AI) holds significant potential to transform HIV prevention and treatment t...

Benchmarking bias in embeddings of healthcare AI models: using SD-WEAT for detection and measurement across sensitive populations.

Bias in vital signs? Machine learning models can learn patients' race or ethnicity from the values of vital signs alone.

OBJECTIVES: To investigate whether machine learning (ML) algorithms can learn racial or ethnic infor...

A Study of Data Augmentation for Learning-Driven Scientific Visualization.

The success of deep learning heavily relies on the large amount of training samples. However, in sci...

CRISPR/Cas-Based Biosensing Strategies for Non-Nucleic Acid Contaminants in Food Safety: Status, Challenges, and Perspectives.

Non-nucleic acid targets (non-NATs), such as heavy metals, toxins, and pesticide residues, pose crit...

Asymptotic theory of in-context learning by linear attention.

Transformers have a remarkable ability to learn and execute tasks based on examples provided within ...

Use of artificial intelligence to prevent aggressions against health professionals.

The alarming rise in assaults against healthcare professionals is a public health and occupational i...

Corrigendum to "Diversity, inclusivity and traceability of mammography datasets used in development of Artificial Intelligence technologies: a systematic review" [Clin Imaging 118 (2025) 110369].

Evaluating accountability, transparency, and bias in AI-assisted healthcare decision- making: a qualitative study of healthcare professionals' perspectives in the UK.