| Title |
Polimadei, Jasmine Simone_MSRS_2025 |
| Alternative Title |
A Literature Review of the Integration and Application of AI-ECG; for Precision Clinical Practices |
| Creator |
Polimadei, Jasmine Simone |
| Collection Name |
Master of Radiologic Sciences |
| Description |
This review focuses on the advancements of AI technology and algorithms applied to electrocardiogram (ECG) interpretation to support clinical decision processes, which aim to improve patient outcomes. The application of Machine Learning (ML) techniques with Deep Learning (DL) subsets such as Recurrent Neural Networks (RNN), and Convolutional Neural Networks (CNN) increases the recognition and identification levels in ECG sensitivity, specificity, accuracy, and precision. |
| Abstract |
The progression and integration of technological advancements in modern-day medical practices continue to propel the diversity of treatment options while improving patient outcomes. Technology implanted with artificial intelligence (AI) generates quality assurance measures with large data collection supporting providers and allied healthcare professionals to implement greater measures with stronger evidence-based medical practices. This review focuses on the advancements of AI technology and algorithms applied to electrocardiogram (ECG) interpretation to support clinical decision processes, which aim to improve patient outcomes. The application of Machine Learning (ML) techniques with Deep Learning (DL) subsets such as Recurrent Neural Networks (RNN), and Convolutional Neural Networks (CNN) increases the recognition and identification levels in ECG sensitivity, specificity, accuracy, and precision. The individual use or clinical application of the AI-ECG algorithm demonstrates great potential to enhance emergency hospital response time management (e.g., door-to-balloon), support precision medical interventions, such as PCI or pharmacological treatments, and promote the appropriate allocation of expensive medical resources during time-sensitive cardiovascular emergencies like ST Elevation Myocardial Infarction (STEMI). Additional support from AI-ECG clinical integrations offers substantial improvements in patient risk assessments and early diagnosis. Although the implementation of AI technology utilized in everyday practice is slow, and with some institutions are more reluctant than others, an established standardized method can provide greater understanding in the technological application that emphasizes a standard of care supporting clinical reasoning and human judgment.; This systematic review abides by the guidelines outlined by PRISMA, ensuring a detailed academic review of documents, articles, and scholarly peer-reviewed journals that are sound resources contributing to a greater understanding of the technological expansion of artificial intelligence (AI) with electrocardiogram (ECG) interpretation. The resources reviewed are intended to summarize current and future applications of AI-ECG ability to provide clarification to improve clinical reasoning and human judgment, optimizing time response to cardiac emergencies, resource allocation, and improving patient outcomes. |
| Subject |
Machine learning; Artificial intelligence; Medical technology |
| Digital Publisher |
Digitized by Special Collections & University Archives, Stewart Library, Weber State University. |
| Date |
2025 |
| Medium |
Thesis |
| Type |
Text |
| Access Extent |
64 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 retains all other rights. For further information:  |
| Source |
University Archives Electronic Records: Master of Radiologic Sciences. Stewart Library, Weber State University |
| Format |
application/pdf |
| ARK |
ark:/87278/s6fdsvwz |
| Setname |
wsu_smt |
| ID |
154099 |
| Reference URL |
https://digital.weber.edu/ark:/87278/s6fdsvwz |
