Latest AI and machine learning research in cultural competence for healthcare professionals.
INTRODUCTION: As artificial intelligence (AI) continues to permeate various sectors, concerns about ...
We introduce the Bias Network Approach (BNA) as a sociotechnical method for AI developers to identif...
Large language models (LLMs) are generative artificial intelligence models that create content on th...
As artificial intelligence (AI) gains prominence in pathology and medicine, the ethical implications...
It stands to reason that the amount and the quality of data are of key importance for setting up acc...
Artificial Intelligence (AI) has been proposed to improve workflow for coronary artery calcium scori...
BACKGROUND: Understanding the mechanisms of algorithmic bias is highly challenging due to the comple...
As new artificial intelligence (AI) tools are being developed and as AI continues to revolutionize h...
Drowning incidents present significant challenges for forensic investigators in determining the exac...
The reliability of automated image interpretation of point-of-care (POC) echocardiography scans depe...
Are the membrane systems able of performing arithmetic operations? In the last dozen years, there we...
Large language models (LLMs) may facilitate and expedite systematic reviews, although the approach t...
The youth mental health crisis is exacerbated by limited access to care and resources. Mobile health...
BACKGROUND: Recent studies have identified significant gaps in equity, diversity, and inclusion (EDI...
In this article, the authors propose a repurposing of the concept of entrustment to help guide the u...
BACKGROUND AND OBJECTIVES: Clinical machine learning (ML) technologies can sometimes be biased and t...
BACKGROUND AND PURPOSE: Recently, artificial intelligence tools have been deployed with increasing s...
(1) Background: Quasi-experimental design has been widely used in causal inference for health policy...
Inductive bias in machine learning (ML) is the set of assumptions describing how a model makes predi...
The goal of debiasing in classification tasks is to train models to be less sensitive to correlation...
Measurement techniques often result in domain gaps among batches of cellular data from a specific mo...