Artificial Intelligence (AI) has emerged as a transformative force in the healthcare sector,
particularly in the realm of remote patient monitoring (RPM). RPM involves the collection …

As large language models continue to advance in Artificial Intelligence (AI), text generation
systems have been shown to suffer from a problematic phenomenon often termed as" …

Large language models (LLMs) have demonstrated promising performance on clinical
knowledge tasks, including the United States Medical Licensing Examination and …

Despite significant technical advances in machine learning (ML) over the past several years,
the tangible impact of this technology in healthcare has been limited. This is due not only to …

Anti-hypotensive treatment, which includes dopamine, dobutamine, epinephrine,
norepinephrine, milrinone, vasopressin, terlipressin, levosimendan, and glucocorticoids, is a …

How can practitioners and clinicians know if a prediction model trained at a different
institution can be safely used on their patient population? There is a large body of evidence …

The primary objective of this commentary was to identify the strengths and weaknesses of AI
technologies, uncover opportunities for improvement, and recognize potential threats that …

Artificial intelligence has revolutionized healthcare, fundamentally altering the conduct of
medical practice, research, and policy. This abstract provides an overview of the latest …

Background: The development of artificial intelligence (AI) approaches impacted drug
discovery, medical imaging, customized diagnostics, and therapeutics. Medicine will be …

Chronic kidney disease (CKD) and other renal diseases represent a significant global health
burden, affecting millions of individuals worldwide. Managing these conditions requires …