Many recent studies have demonstrated that environmental contexts, both social and
physical, have an important impact on child and adolescent neural and behavioral …

The integration of artificial intelligence (AI) into health care offers the potential to enhance
patient care, improve diagnostic precision, and broaden access to health-care services …

Objective We developed and externally validated a machine-learning model to predict
postpartum depression (PPD) using data from electronic health records (EHRs). Effort is …

Health care organizations are grappling with how to discover and mitigate the risks of
artificial intelligence (AI) and associated algorithms worsening racial, ethnic, and …

Contrary to traditional deterministic notions of algorithmic fairness, this paper argues that
fairly allocating scarce resources using machine learning often requires randomness. We …

The field of electrophysiology (EP) has benefited from numerous seminal innovations and
discoveries that have enabled clinicians to deliver therapies and interventions that save …

In the high-stakes realm of healthcare, ensuring fairness in predictive models is crucial.
Electronic Health Records (EHRs) have become integral to medical decision-making, yet …

Large language models (LLMs) have been proposed to support many healthcare tasks,
including disease diagnostics and treatment personalization. While AI models may be …

Background: Automatic transdiagnostic risk calculators can improve detection of individuals
at risk of psychosis. However, they rely on a single point in time assessment and can be …

Abstract The fusion of Artificial Intelligence (AI) and healthcare heralds a new era of
innovation and transformation, yet it is not without its ethical quandaries. This …