AI in Radiology: Bridging the Gap Between Technology and Patient Care

Abstract

Artificial Intelligence (AI) integration in healthcare, particularly within radiology, has grown rapidly, with 400 out of 520 FDA-approved AI algorithms explicitly designed for radiological applications as of 2023. AI has shown significant potential for enhancing healthcare delivery and improving patient outcomes (AHA, 2023); however, understanding the barriers, facilitators, and implications of AI implementation in radiology remains fragmented across existing studies. This study investigates AI\u27s impact on radiology in three critical areas: diagnostic accuracy, interpretation times, and clinical workflow efficiency. We synthesize key findings regarding AI\u27s contributions to radiology practices through a comprehensive literature review of 29 articles published between 2015 and 2024, sourced from databases including PubMed, EBSCOhost, and Google Scholar