Advances in computer science and data-analytic methods are driving a new era in mental
health research and application. Artificial intelligence (AI) technologies hold the potential to …

Developing AI tools that preserve fairness is of critical importance, specifically in high-stakes
applications such as those in healthcare. However, health AI models' overall prediction …

Background Developing effective and generalizable predictive models is critical for disease
prediction and clinical decision-making, often requiring diverse samples to mitigate …

While machine learning (ML) models hold promise for enhancing the management of acute
kidney injury (AKI) in sepsis patients, creating models that are equitable and unbiased is …

Personal informatics (PI) systems, powered by smartphones and wearables, enable people
to lead healthier lifestyles by providing meaningful and actionable insights that break down …

Fairness of artificial intelligence and machine learning models, often caused by imbalanced
datasets, has long been a concern. While many efforts aim to minimize model bias, this …

Aims/hypothesis To determine the extent to which diabetic retinopathy severity stage may be
classified using machine learning (ML) and commonly used clinical measures of visual …

Methods Six electronic databases (PubMed, Scopus, IEEE, Google Scholar, EMBASE, and
Cochrane) were systematically searched on October 3, 2023. Inclusion criteria were peer …

Recent advances in the field of large language models (LLMs) underline their high potential
for applications in a variety of sectors. Their use in healthcare, in particular, holds out …

OBJECTIVE: Ensuring that machine learning (ML) algorithms are safe and effective within all
patient groups, and do not disadvantage particular patients, is essential to clinical decision …