Utilization of Artificial Intelligence in Radiography for Improved Diagnosis and Treatment Planning

Thesis Abstract

Abstract
This thesis investigates the utilization of Artificial Intelligence (AI) in radiography to enhance the accuracy and efficiency of diagnosis and treatment planning in medical imaging. The integration of AI technologies in radiography has the potential to revolutionize the field by providing automated analysis, pattern recognition, and decision-making support to radiologists and healthcare professionals. The study explores how AI algorithms can be applied to various imaging modalities, such as X-rays, CT scans, and MRIs, to assist in the detection, classification, and interpretation of abnormalities and diseases. The research begins with a comprehensive review of the current literature on AI in radiography, highlighting the advancements, challenges, and potential applications of AI technologies in medical imaging. The literature review also examines the impact of AI on radiology practices, patient outcomes, and healthcare delivery systems. Following the literature review, the research methodology section outlines the approach taken to investigate the effectiveness and feasibility of AI in radiography. The methodology includes data collection methods, AI algorithm selection criteria, model development, training, and validation processes. Additionally, ethical considerations, data privacy, and regulatory compliance in AI applications in radiography are addressed. The discussion of findings section presents the results of the study, including the performance evaluation of AI algorithms in diagnosing and planning treatments for various medical conditions. The findings highlight the strengths and limitations of AI technologies in radiography and their potential impact on clinical decision-making and patient care. In conclusion, this thesis summarizes the key findings and implications of the research, emphasizing the significance of AI in radiography for improving diagnostic accuracy, treatment planning efficiency, and patient outcomes. The study contributes to the growing body of knowledge on AI applications in medical imaging and provides insights into the future directions and challenges in integrating AI technologies into radiology practices. Keywords Artificial Intelligence, Radiography, Medical Imaging, Diagnosis, Treatment Planning, Machine Learning, Deep Learning, Healthcare Technology.