Introduction Healthcare systems are complex and challenging for all stakeholders, but artificial
intelligence (AI) has transformed various fields, including healthcare, with the potential to …

… terms, including “artificial intelligence,” “machine learning,” and “… likely to use “artificial
intelligence” or “machine learning” in … terms related to clinical implementation, including “clinical,” “…

… pertaining to artificial intelligence (AI) within the domain of clinical practice. Artificial intelligence
has … One of the most important benefits of AI in clinical practice is its ability to investigate …

… on the benefits, future opportunities and risks of established artificial intelligence applications
in clinical practice on physicians, healthcare institutions, medical education, and bioethics. …

… MS clinical practice. First, we present a brief introduction on the topic of AI. Then, we examine
ongoing research on the use of AI techniques in MS for clinical … on clinical applications. We …

… assessments, namely whole slide imaging and artificial intelligence (AI)–based solutions, …
this technology may face if adopted in clinical settings. The traditional role of pathologists in …

Artificial intelligence (AI) has the potential to significantly disrupt the way radiology will be
practiced in the near future, but several issues need to be resolved before AI can be widely …

Objective To investigate the applications of artificial intelligence (AI) in diagnosis, treatment
and outcome predictionin urologic diseases and evaluate its advantages over traditional …

… When supervised machine learning was applied to 46 clinical variables from 397 HFpEF …
that could benefit from therapies that failed to show survival benefits in clinical trial cohorts. …

The multitude of artificial intelligence (AI)–based solutions, vendors, and platforms poses a
challenging proposition to an already complex clinical radiology practice. Apart from …