This perspective highlights the importance of addressing social determinants of health
(SDOH) in patient health outcomes and health inequity, a global problem exacerbated by …

Background Mental health difficulties are highly prevalent worldwide. Passive sensing
technologies and applied artificial intelligence (AI) methods can provide an innovative …

Objective To identify factors influencing implementation of machine learning algorithms
(MLAs) that predict clinical deterioration in hospitalized adult patients and relate these to a …

Background Despite substantial progress in AI research for healthcare, translating research
achievements to AI systems in clinical settings is challenging and, in many cases …

Background Efforts are underway to capitalize on the computational power of the data
collected in electronic medical records (EMRs) to achieve a learning health system (LHS) …

The integration of artificial intelligence (AI) into health care has seen remarkable
advancements, with applications extending to animal health. This article explores the …

Accurate and rapid disease detection is necessary to manage health problems early. Rapid
increases in data amount and dimensionality caused challenges in many disciplines, with …

Artificial intelligence (AI) has experienced fast enlargement in current years. The term AI
refers to the intelligence exhibited by machines, contrasting with that of humans or other …

Research on the applications of artificial intelligence (AI) tools in medicine has increased
exponentially over the last few years but its implementation in clinical practice has not seen …

Background The field of implementation science was developed to address the significant
time delay between establishing an evidence-based practice and its widespread use …