| Title |
MSRS Summer 2025 Cohort |
| Alternative Title |
Perceptions and Realities: Investigating Barriers to Artificial Intelligence (AI) Integration Among Medical Imaging Professionals within the United States |
| Creator |
Fisher, Adam; Harbour, Codi; Kiger, Katlyn; Oveson, Michael; Poss, Kimberly; Scott, Emilee |
| Contributors |
Nolan, Tanya (advisor) |
| Collection Name |
Master of Radiologic Sciences |
| Description |
This study surveyed 253 radiology professionals across the U.S. to assess perceptions of artificial intelligence (AI) use, identifying significant differences based on age, facility size, and professional role. Findings reveal limited AI adoption, with barriers linked to demographic and workplace factors, suggesting the need for broader education and expanded research into AI implementation in radiology. |
| Abstract |
Artificial intelligence (AI) is an ever-evolving factor in today's world, especially within the radiology department of healthcare. While AI can be seen with positive results and adequate usage around the globe, there seems to be little known or recorded usage within the United States. To further investigate why this might be, quantitative plans were made to evaluate various radiology personnel and their perceptions about AI. A Likert-style survey was built and questioned participants about their perceptions regarding AI usage, potential barriers of AI implementation, associated AI risks and benefits, as well as various demographic information. The surveys were sent out through social media outlets and through emails through a convenient sampling fashion. There were 253 surveys collected back from various regions of the United States, and were filled out by technologists, managers, radiologists and radiologist extenders. The data from the surveys were then cleaned up and evaluated through one-way ANOVA and correlation statistics to check for significant findings between the research questions and the variables. There was a significant difference between facility size and the sum of AI usage (p=<.001). Significant findings were found in the overall sum of barriers based on age of participants (p=<.001). There was a significant difference between age and benefits (p=<.001). There was a significant difference between age and risks (p=<.001). The research showed that there was a limited use of AI across the United States, and that there were several barriers identified associated with factors, such as age, years worked, facility size and roles within radiology departments. Future research should focus on reaching larger sampling sizes, seek to avoid further convenience sampling, and focus on getting more radiology departments better educated on current AI systems and their associated benefits. |
| Subject |
Artificial intelligence; Medical technology |
| Digital Publisher |
Digitized by Special Collections & University Archives, Stewart Library, Weber State University. |
| Date |
2025-08 |
| Medium |
Thesis |
| Type |
Text |
| Access Extent |
81 page pdf |
| Conversion Specifications |
Adobe Acrobat |
| Language |
eng |
| Rights |
The author has granted Weber State University Archives a limited, non-exclusive, royalty-free license to reproduce his or her thesis, in whole or in part, in electronic or paper form and to make it available to the general public at no charge. The author |
| Source |
University Archives Electronic Records: Master of Radiologic Sciences. Stewart Library, Weber State University |
| Format |
application/pdf |
| ARK |
ark:/87278/s6rhy13j |
| Setname |
wsu_smt |
| ID |
155062 |
| Reference URL |
https://digital.weber.edu/ark:/87278/s6rhy13j |
