Investigating the Impact of Artificial Intelligence on Radiography Practice: A Case Study.

Thesis Abstract

Abstract
This thesis investigates the impact of artificial intelligence (AI) on radiography practice through a case study approach. The integration of AI technologies in healthcare has transformed various aspects of radiography practice, leading to improved efficiency, accuracy, and patient outcomes. In this study, the focus is on understanding how AI applications are being utilized in radiography, exploring the benefits and challenges associated with their implementation, and assessing the overall impact on healthcare delivery and patient care. The research begins with an introduction to the topic, providing a background overview of AI in radiography practice. The problem statement highlights the need to examine the effects of AI on traditional radiography methods and the potential implications for healthcare professionals and patients. The objectives of the study include evaluating the effectiveness of AI tools in radiography, identifying key challenges in their deployment, and analyzing the implications for radiography practice. The study acknowledges certain limitations, such as the availability of data and the evolving nature of AI technologies in healthcare. The scope of the research focuses on a specific case study within a healthcare institution to provide a detailed analysis of AI integration in radiography practice. The significance of the study lies in its contribution to the existing body of knowledge on AI applications in healthcare and its implications for radiography professionals and patients. The structure of the thesis is outlined, with Chapter One providing an introduction to the research topic, background information, problem statement, objectives, limitations, scope, significance, and definition of key terms. Chapter Two consists of a comprehensive literature review, exploring ten key themes related to AI in radiography practice, including technology applications, benefits, challenges, and ethical considerations. Chapter Three details the research methodology, including research design, data collection methods, sampling techniques, data analysis procedures, and ethical considerations. The chapter also outlines the theoretical framework guiding the study and describes the case study approach adopted for data collection and analysis. Chapter Four presents a detailed discussion of the findings, examining the impact of AI on radiography practice based on the case study data. The chapter evaluates the effectiveness of AI tools, identifies challenges encountered during implementation, and discusses the implications for healthcare professionals and patients. Finally, Chapter Five provides a conclusion and summary of the thesis, highlighting key findings, implications for practice, and recommendations for future research. The thesis contributes to a better understanding of the impact of AI on radiography practice and offers insights into how healthcare institutions can leverage these technologies to enhance patient care and outcomes. In conclusion, this thesis provides valuable insights into the evolving landscape of radiography practice in the era of artificial intelligence, shedding light on the opportunities and challenges associated with AI integration in healthcare.