Outlet Title

Issues in Information Systems

Document Type

Article

Publication Date

2025

Abstract

Artificial intelligence (AI) is progressively integrated into radiologists' processes, improving diagnostic precision, decision-making, and operational efficiency. This systematic literature review (SLR) is a Meta- analysis utilizing the PRISMA framework that investigates the transformative impact of AI in radiology by analyzing studies published from January 2019 to December 2024 across the academic databases of ACM Digital Library, IEEE/IET, Elsevier ScienceDirect, ProQuest, and PubMed. The research employs the People, Process, Data, and Technology (2PDT) framework to classify AI technologies and assess their effects on patient outcomes, radiologists' experiences, and healthcare system performance. The findings indicate the substantial contributions of AI, including enhanced diagnostic accuracy via deep learning models, workflow automation, and facilitation of structured decision-making. AI advancements in identifying diseases, like cancer and tuberculosis screening, correspond with the Quadruple Aim (4Aim) by augmenting patient experience, decreasing costs, improving the work-life of healthcare providers, and promoting population health. This study offers practical insights for practitioners and Information Systems researchers, highlighting new trends, gaps, and recommendations to enhance the integration of AI in radiology practice. Future research must investigate ethical implications, long-term effects, and seamless integration to optimize the capability of AI to transform healthcare delivery.

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