Background Artificial intelligence (AI) is often heralded as a potential disruptor that will
transform the practice of medicine. The amount of data collected and available in health …

There is a large proliferation of complex data-driven artificial intelligence (AI) applications in
many aspects of our daily lives, but their implementation in healthcare is still limited. This …

Implementation of artificial intelligence systems for healthcare is challenging. Understanding
the barriers and implementation strategies can impact their adoption and allows for better …

Healthcare institutions are progressively integrating artificial intelligence (AI) into their
operations. The extraordinary potential of AI is restricted by insufficient medical data for AI …

Primary Care Physicians (PCPs) are the first point of contact in healthcare. Because PCPs
face the challenge of managing diverse patient populations while maintaining up-to-date …

Background There has been a substantial increase in the development of artificial
intelligence (AI) tools for clinical decision support. Historically, these were mostly knowledge …

Background There is a growing enthusiasm for machine learning (ML) among academics
and health care practitioners. Despite the transformative potential of ML-based applications …

Medical data in online groups and social media contain valuable information, which is
provided by both healthcare professionals and patients. In fact, patients can talk freely and …

Clinicians dedicate significant time to clinical documentation, incurring opportunity cost.
Artificial intelligence (AI) tools promise to improve documentation quality and efficiency. This …

Objective Combining text mining (TM) and clinical decision support (CDS) could improve
diagnostic and therapeutic processes in clinical practice. This review summarizes current …