Date of Award

2025

Degree Name

Healthcare Administration

College

College of Business

Type of Degree

M.S.

Document Type

Research Paper

First Advisor

Alberto Coustasse, Dr.PH. MD, MBA, MPH

Abstract

Introduction: Artificial intelligence (AI) has increasingly transformed radiologic practice by improving diagnostic accuracy, streamlining workflows, and reducing interpretation errors. As AI integration has expanded across imaging modalities, questions have emerged regarding its effectiveness compared to traditional radiologist-only interpretation.

Purpose of Study: The purpose of this study has been to evaluate the impact of AI-assisted radiology on diagnostic accuracy, efficiency, and error reduction, while also assessing clinician perceptions of AI as a collaborative tool in imaging analysis.

Methodology: This qualitative study has used a systematic review of peer-reviewed literature published between 2015 and 2025, following PRISMA guidelines, combined with an interview with a licensed radiologic technologist. Forty studies were initially identified, and 30 met inclusion criteria related to diagnostic performance, turnaround time, and interpretive reliability. The interview provided experiential insight into the clinical use of AI.

Results: The systematic review has shown that AI systems have consistently increased sensitivity and specificity across various imaging domains, including pulmonary, oncologic, neurologic, and musculoskeletal assessments. Multiple studies have demonstrated that AI has detected abnormalities missed during manual review, thereby reducing diagnostic error and supporting quicker clinical intervention. Additionally, AI-assisted workflows have been associated with reduced interpretation times and decreased radiologist workload. The technologist interview supported these findings, noting that AI has improved consistency and accuracy in image review while emphasizing the continued need for human oversight.

Discussion: The combined findings have indicated that AI has strengthened radiologic practice by enhancing both diagnostic and operational outcomes. However, the literature and interview have also highlighted the necessity of maintaining clinician involvement to contextualize AI-generated outputs, mitigate algorithmic bias, and ensure patient safety. Ongoing education, regulatory development, and long-term performance monitoring have been identified as essential components of responsible AI integration.

Conclusion: This study has demonstrated that AI has served as a valuable adjunct to radiologists by improving diagnostic performance, reducing error rates, and supporting efficient clinical workflows. While AI continues to evolve, radiologists and technologists remain central to ensuring accurate interpretation and optimal patient outcomes.

Subject(s)

Health services administration.

Health facilities -- Business management.

Artificial intelligence.

Artificial intelligence -- Medical applications.

Radiology.

Radiology -- Diagnosis.

Diagnostic imaging.

Medical errors -- Prevention.

Diagnosis -- Decision making.

Radiology -- Decision making.

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