AI and Radiology: Exploring People, Process, and Technology through a Meta-Analysis

Presentation Type

Poster

Track

Health Care Application

Abstract

Artificial Intelligence (AI) is increasingly being integrated into radiologists' workflows, enhancing their ability to make effective, data-driven decisions for patient care. This PRISMA-based meta-analysis examines systematic literature reviews (SLRs) to identify and expand upon the existing body of research regarding the types and roles of AI utilized in radiology. Covering academic databases from January 2019 to December 2024, the study employs a conceptual model based on the dimensions of People, Process, and Technology (PPT) to categorize the findings.

The analysis highlights the evolving applications of AI in supporting radiologists' work and the specific roles benefiting from AI integration. This research provides valuable insights for practitioners and Information Systems (IS) researchers by identifying key trends and gaps in the literature. The study concludes with recommendations to enhance the understanding, adoption, and successful integration of AI into the complex professional responsibilities of radiologists. Initial findings suggest recommendations for improving the support of integration of AI into the categories of people, processes, and technology.

Start Date

2-7-2025 1:00 PM

End Date

2-7-2025 2:30 PM

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Feb 7th, 1:00 PM Feb 7th, 2:30 PM

AI and Radiology: Exploring People, Process, and Technology through a Meta-Analysis

Volstorff A

Artificial Intelligence (AI) is increasingly being integrated into radiologists' workflows, enhancing their ability to make effective, data-driven decisions for patient care. This PRISMA-based meta-analysis examines systematic literature reviews (SLRs) to identify and expand upon the existing body of research regarding the types and roles of AI utilized in radiology. Covering academic databases from January 2019 to December 2024, the study employs a conceptual model based on the dimensions of People, Process, and Technology (PPT) to categorize the findings.

The analysis highlights the evolving applications of AI in supporting radiologists' work and the specific roles benefiting from AI integration. This research provides valuable insights for practitioners and Information Systems (IS) researchers by identifying key trends and gaps in the literature. The study concludes with recommendations to enhance the understanding, adoption, and successful integration of AI into the complex professional responsibilities of radiologists. Initial findings suggest recommendations for improving the support of integration of AI into the categories of people, processes, and technology.