Implementation of Artificial Intelligence in Radiography for Improved Diagnostic Accuracy

Thesis Abstract

Abstract
The field of radiography has seen significant advancements in recent years, with the integration of artificial intelligence (AI) emerging as a promising avenue for enhancing diagnostic accuracy and efficiency. This thesis explores the implementation of AI in radiography to improve diagnostic accuracy, focusing on its potential impact on healthcare outcomes and patient care. The study begins with an introduction to the background and context of AI in radiography, highlighting the current challenges in diagnostic accuracy and the need for innovative solutions. The problem statement underscores the limitations of traditional radiographic techniques and the growing demand for more accurate and timely diagnoses. The objectives of the study are to evaluate the effectiveness of AI in improving diagnostic accuracy, identify the limitations and challenges associated with its implementation, and assess the scope and significance of its impact on radiography practice. The research methodology employed encompasses a comprehensive literature review of existing studies on AI in radiography, followed by the development of a framework for evaluating AI systems in a clinical setting. The study also incorporates interviews and surveys with radiography professionals to gather insights on their experiences and perspectives regarding AI integration. The findings of the study reveal that AI technologies have the potential to significantly enhance diagnostic accuracy in radiography by assisting radiographers in interpreting imaging data and detecting abnormalities with greater precision. However, challenges such as data security, ethical considerations, and regulatory compliance pose barriers to the widespread adoption of AI in clinical practice. The discussion of findings delves into these challenges and proposes recommendations for overcoming them, emphasizing the importance of collaboration between healthcare providers, technology developers, and regulatory bodies. In conclusion, the study underscores the transformative potential of AI in radiography for improving diagnostic accuracy and patient outcomes. By leveraging AI technologies to augment radiographic interpretation, healthcare providers can deliver more accurate and timely diagnoses, leading to enhanced patient care and treatment outcomes. The thesis contributes to the growing body of research on AI in healthcare and provides valuable insights for radiography professionals, policymakers, and stakeholders seeking to leverage AI technologies for improved diagnostic accuracy in radiography practice.