| Title |
MSRS Fall 2024 Cohort |
| Alternative Title |
REVOLUTIONIZING RADIOLOGY: A SYSTEMATIC REVIEW OF HOW ARTIFICIAL INTELLIGENCE CAN IMPACT THE AMERICAN HEALTHCARE SYSTEM |
| Creator |
Dangleis, Nicki; Duncan, Kristin; Ewing, Benjamin; Hassan, Jama; Kurtek, Logan; McBee, Kelli; Mick, Kyndal; Moffett, Jay; Phillips, Keri; Riquelme, Etilia; Schock, Holli; Smith, Lynn; Verzwyvelt, Cassandra |
| Collection Name |
Master of Radiologic Sciences |
| Description |
This systematic review provides a qualitative analysis of the current AI landscape in imaging, covering present uses of AI technology, as well as its successes and limitations. |
| Abstract |
The use of artificial intelligence (AI) in radiology marks a significant breakthrough for the future of medical imaging technology. As AI technology advances and becomes more integrated into healthcare, its influence on radiology continues to evolve at a rapid pace. However, despite the rapid growth, the United States seems to be lagging in AI implementation in comparison to other medically advanced countries. This systematic review provides a qualitative analysis of the current AI landscape in imaging, covering present uses of AI technology, as well as its successes and limitations. Articles and studies from both the United States and around the world reveal multiple countries are effectively improving patient care. Quantitative data revealed a 60% reduction in missed diagnoses of lung conditions through deep learning algorithms analyzing chest radiographs. Additionally, AI increased processing speed by 21%, notably improving urgent care scenarios like stroke assessments, thereby expediting the decision-making process for critical interventions. These results show that while AI is revolutionizing radiology by enhancing diagnostic speed and accuracy, its integration also demands careful handling of challenges such as ethical concerns, legal framework, financial hesitations, and shifts in departmental roles. Looking ahead, it is essential to focus on developing educational programs, enhancing data-sharing practices, and setting ethical standards to fully leverage AI's potential in transforming radiology and improving patient care. Future research should not only validate and improve AI's capabilities but also create strong frameworks to allow proper implementation and growth. |
| Subject |
Artificial intelligence; Medicine; Medical technology; Ethics |
| Digital Publisher |
Stewart Library, Weber State University, Ogden, Utah, United States of America |
| Date |
2024 |
| Medium |
Thesis |
| Type |
Text |
| Access Extent |
2.1 MB; 70 page pdf |
| Language |
eng |
| Rights |
The author has granted Weber State University Archives a limited, non-exclusive, royalty-free license to reproduce his or her theses, in whole or in part, in electronic or paper form and to make it available to the general public at no charge. The author retains all other rights. |
| Source |
University Archives Electronic Records: Master of Radiologic Sciences. Stewart Library, Weber State University |
| Format |
application/pdf |
| ARK |
ark:/87278/s6b3r11m |
| Setname |
wsu_smt |
| ID |
143576 |
| Reference URL |
https://digital.weber.edu/ark:/87278/s6b3r11m |
