Background It has been rightfully emphasized that the use of AI for clinical decision making
could amplify health disparities. An algorithm may encode protected characteristics, and …

Background The timely detection of Alzheimer's disease and other related dementias
(ADRD) is suboptimal. Digital data already stored in electronic health records (EHR) offer …

The field of mobile, wearable, and ubiquitous computing (UbiComp) is undergoing a
revolutionary integration of machine learning. Devices can now diagnose diseases, predict …

Research on artificial intelligence (AI) has emerged as a promising field that has the
potential to improve patient outcomes, for example, by optimizing timing of antibiotic therapy …

Artificial intelligence systems are increasingly being applied to healthcare. In surgery, AI
applications hold promise as tools to predict surgical outcomes, assess technical skills, or …

As algorithmic decision making is increasingly deployed in every walk of life, many
researchers have raised concerns about fairness-related bias from such algorithms. But …

Fairness is central to the ethical and responsible development and use of AI systems, with a
large number of frameworks and formal notions of algorithmic fairness being available …

The emphasis on fairness in predictive healthcare modeling has increased in popularity as
an approach for overcoming biases in automated decision-making systems. The aim is to …

This study presents a machine learning (ML) pipeline for clinical data classification in the
context of a 30-day readmission problem, along with a fairness audit on subgroups based …

Abstract In the United States, primary open-angle glaucoma (POAG) is the leading cause of
blindness, especially among African American and Hispanic individuals. Deep learning has …