Cultural Competence

AI Can Be a Powerful Social Innovation for Public Health if Community Engagement Is at the Core.

There is a critical need for community engagement in the process of adopting artificial intelligence (AI) technologies in public health. Public health practitioners and researchers have historically innovated in areas like vaccination and sanitation but have been slower in adopting emerging technologies such as generative AI. However, with increasingly complex funding, programming, and research re...

On spectral bias reduction of multi-scale neural networks for regression problems.

In this paper, we derive diffusion equation models in the spectral domain to study the evolution of the training error of two-layer multiscale deep neural networks (MscaleDNN) (Cai and Xu, 2019; Liu et al., 2020), which is designed to reduce the spectral bias of fully connected deep neural networks in approximating oscillatory functions. The diffusion models are obtained from the spectral form of ...

Machine learning algorithms for predicting PTSD: a systematic review and meta-analysis.

This study aimed to compare and evaluate the prediction accuracy and risk of bias (ROB) of post-traumatic stress disorder (PTSD) predictive models. We...

Responsible Design, Integration, and Use of Generative AI in Mental Health.

Generative artificial intelligence (GenAI) shows potential for personalized care, psychoeducation, and even crisis prediction in mental health, yet re...

CPJN: News recommendation with a content and popularity joint network.

Users may click on a news because they are interested in its content or because the news contains important information and is very popular. Modeling ...

Reducing bias in source-free unsupervised domain adaptation for regression.

Due to data privacy and storage concerns, Source-Free Unsupervised Domain Adaptation (SFUDA) focuses on improving an unlabelled target domain by lever...

An image dataset for analyzing tea picking behavior in tea plantations.

Tea is an important economic product in China, and tea picking is a key agricultural activity. As the practice of tea picking in China gradually shift...

Exploring the impact of land use on bird diversity in high-density urban areas using explainable machine learning models.

Amid rapid urbanization, land use shifts in cities globally have profound effects on ecosystems and biodiversity. Birds, as a crucial component of urb...

Machine learning assessment of dredging impacts on the phytoplankton community on the Brazilian equatorial margin: A multivariate analysis.

Dredging in estuarine systems significantly impacts phytoplankton communities, with suspended particulate matter (SPM) and dissolved aluminum (Al) ser...

Disentangled Active Learning on Graphs.

Active learning on graphs (ALG) has emerged as a compelling research field due to its capacity to address the challenge of label scarcity. Existing AL...

Sex-Based Bias in Artificial Intelligence-Based Segmentation Models in Clinical Oncology.

Artificial intelligence (AI) advancements have accelerated applications of imaging in clinical oncology, especially in revolutionizing the safe and ac...

Designing a Multimodal and Culturally Relevant Alzheimer Disease and Related Dementia Generative Artificial Intelligence Tool for Black American Informal Caregivers: Cognitive Walk-Through Usability Study.

BACKGROUND: Many members of Black American communities, faced with the high prevalence of Alzheimer disease and related dementias (ADRD) within their ...

Humor as a window into generative AI bias.

A preregistered audit of 600 images by generative AI across 150 different prompts explores the link between humor and discrimination in consumer-facin...

Schizophrenia more employable than depression? Language-based artificial intelligence model ratings for employability of psychiatric diagnoses and somatic and healthy controls.

Artificial Intelligence (AI) assists recruiting and job searching. Such systems can be biased against certain characteristics. This results in potenti...

Searching to extrapolate embedding for out-of-graph node representation learning.

Out-of-graph node representation learning aims at learning about newly arrived nodes for a dynamic graph. It has wide applications ranging from commun...

A Plasma Proteomics-Based Model for Identifying the Risk of Postpartum Depression Using Machine Learning.

Postpartum depression (PPD) poses significant risks to maternal and infant health, yet proteomic analyses of PPD-risk women remain limited. This study...

Bias Mitigation in Primary Health Care Artificial Intelligence Models: Scoping Review.

BACKGROUND: Artificial intelligence (AI) predictive models in primary health care have the potential to enhance population health by rapidly and accur...

Bias in adjudication: Investigating the impact of artificial intelligence, media, financial and legal institutions in pursuit of social justice.

The latest global progress report highlights numerous challenges in achieving justice goals, with bias in artificial intelligence (AI) emerging as a s...

Why robot embodiment matters: questions of disability, race and intersectionality in the design of social robots.

A growing minority of those with disabilities are people of color (POC), with, for example, autism diagnosis rates now higher for children of color th...

Machine learning derived retinal pigment score from ophthalmic imaging shows ethnicity is not biology.

Few metrics exist to describe phenotypic diversity within ophthalmic imaging datasets, with researchers often using ethnicity as a surrogate marker fo...