Utilization of Artificial Intelligence in Radiography for Early Detection of Pathologies

Thesis Abstract

Abstract
The field of radiography has seen significant advancements in recent years, with the emergence of artificial intelligence (AI) technologies offering new opportunities for enhancing the detection of pathologies at an early stage. This thesis explores the utilization of AI in radiography for early detection of pathologies, with a primary focus on improving diagnostic accuracy and patient outcomes. The research methodology involved a comprehensive literature review, data collection, and analysis to investigate the efficacy of AI algorithms in radiographic imaging. Chapter One provides an introduction to the study, offering an overview of the background, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of terms related to the topic. The literature review in Chapter Two synthesizes ten key studies on the application of AI in radiography, highlighting the current state of research, challenges, and opportunities in the field. Chapter Three details the research methodology, outlining the study design, data collection methods, AI algorithms used, validation techniques, ethical considerations, and potential biases. The discussion of findings in Chapter Four presents a detailed analysis of the results obtained from applying AI in radiography for early detection of pathologies. The chapter examines the performance of AI algorithms in comparison to traditional diagnostic methods, discussing the strengths and limitations of AI in this context. In conclusion, Chapter Five provides a summary of the thesis, highlighting the key findings, implications for practice, and recommendations for future research. The study demonstrates the potential of AI technologies to revolutionize radiography practice by improving diagnostic accuracy, reducing errors, and facilitating early detection of pathologies. The findings of this research contribute to the growing body of knowledge on the integration of AI in radiography and offer insights into the transformative impact of AI on healthcare delivery. Overall, this thesis underscores the importance of leveraging AI in radiography for early detection of pathologies, emphasizing the need for continued research and innovation in this area to enhance patient care and outcomes. The integration of AI technologies holds promise for revolutionizing radiographic imaging practices and advancing the field of healthcare diagnostics.