Date of Award

2024

Degree Name

Healthcare Administration

College

College of Business

Type of Degree

M.S.

Document Type

Research Paper

First Advisor

Alberto Coustasse

Abstract

Introduction: The use of artificial intelligence in radiology has helped radiologists identify patterns and abnormalities in medical images to diagnose and treat patients. Deep learning and machine learning algorithms have been used to assist physicians in detecting features that are not noticeable to the human eye. The FDA has approved almost 400 AI algorithms for radiology and estimated that the market for AI in medical imaging would grow from $21.48 billion in 2018 to $264.85 billion in 2028.

Purpose of the Study: The purpose of this research was to evaluate the use of artificial intelligence in radiology to determine its impact on diagnostic accuracy, interpretation time, and clinical workflow efficiency in imaging acquisition.

Methodology: The intended methodology for this qualitative study was a literature review with a semi-structured interview with an expert in AI used in radiology. Three databases were used to collect 8,349 total sources. The sources gathered from databases were reviewed and reduced to 29 sources that were limited to the English language and were published from the years 2015 through 2024. The last source was gathered from a semi-structured interview. Of the sources used, 20 were used in the results section.

Results: The research showed that the use of AI in radiology has improved diagnostic accuracy and interpretation time by providing physicians with additional information. Radiologists were able to improve their interpretation time by seconds using AI systems, which allowed for patients to be diagnosed and treated sooner. Clinical workflow efficiency showed improvement in getting better images and lowering patients contrast dosage, but it was shown that some AI systems created additional steps for technologists, resulting in a longer process.

Discussion/Conclusion: AI served as a second pair of eyes to detect abnormalities that could be missed by the human eye, which improved diagnostic accuracy and interpretation time. The literature review and semi-structured interview had mixed findings on whether AI improved imaging acquisition. Further study is needed to evaluate how artificial intelligence impacts radiology as it continues to grow.

Subject(s)

Health services administration.

Health facilities -- Business Management.

Artificial Intelligence -- Medicine.

Machine Learning -- Medicine.

Radiology.

Diagnostic imaging.

Workflow.

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