Thesis Abstract

Abstract
The integration of Artificial Intelligence (AI) technologies in radiography has revolutionized the field of medical imaging, offering the potential to enhance diagnostic accuracy and efficiency. This thesis explores the implementation of AI in radiography to improve diagnostic accuracy, focusing on its applications, challenges, and impacts in clinical practice. The study begins with a comprehensive literature review to examine the current state of AI in radiography, highlighting key trends, advancements, and limitations in the field. Subsequently, the research methodology section outlines the approach taken to investigate the effectiveness of AI tools in improving diagnostic accuracy in radiography. Through the collection and analysis of data from various sources, including case studies and empirical research, the study presents a detailed discussion of findings regarding the impact of AI on radiographic interpretation. The results reveal the potential of AI algorithms to assist radiologists in detecting anomalies, reducing interpretation errors, and enhancing overall diagnostic accuracy. Moreover, the study addresses the challenges and limitations associated with the integration of AI in radiography, such as data privacy concerns, algorithm bias, and the need for continuous training and validation. Furthermore, the thesis explores the implications of AI implementation in radiography for healthcare systems, patient outcomes, and radiology practice. The findings underscore the significance of collaboration between AI systems and radiologists to optimize diagnostic workflows and improve patient care. The conclusion reflects on the key insights gained from the research, emphasizing the transformative potential of AI technologies in enhancing diagnostic accuracy and clinical decision-making in radiography. In summary, this thesis contributes to the existing body of knowledge on the implementation of AI in radiography for improved diagnostic accuracy. By investigating the applications, challenges, and impacts of AI technologies in medical imaging, the study sheds light on the opportunities and considerations associated with integrating AI into radiology practice. Ultimately, the findings underscore the critical role of AI in advancing the field of radiography and improving the quality of patient care through enhanced diagnostic precision and efficiency.

Thesis Overview

The project titled "Implementation of Artificial Intelligence in Radiography for Improved Diagnostic Accuracy" aims to explore the integration of artificial intelligence (AI) technology into the field of radiography to enhance diagnostic accuracy. In recent years, AI has emerged as a powerful tool in various industries, including healthcare, due to its ability to process vast amounts of data and identify patterns that human experts may overlook. This project seeks to leverage AI algorithms to analyze medical imaging data in radiography, with the goal of improving the accuracy and efficiency of diagnostic processes. The research will begin with a comprehensive literature review to explore existing studies and technologies related to AI in radiography and diagnostic imaging. This review will provide valuable insights into the current state of the field, identify gaps in knowledge, and establish a foundation for the research methodology. The research methodology will involve the collection and analysis of medical imaging data, including X-rays, CT scans, and MRI images, using AI algorithms. The AI models will be trained on a dataset of medical images to learn to recognize patterns associated with various medical conditions. The performance of the AI models will be evaluated based on their ability to accurately diagnose and classify medical conditions compared to traditional diagnostic methods. The findings of the study will be presented and discussed in Chapter 4, focusing on the effectiveness of AI in improving diagnostic accuracy in radiography. The discussion will highlight the strengths and limitations of AI technology in this context, as well as potential challenges and opportunities for further research and implementation. Finally, Chapter 5 will provide a summary of the project, including key findings, conclusions, and recommendations for future research and practical applications. The project aims to contribute to the growing body of knowledge on the integration of AI in radiography and its potential to revolutionize diagnostic processes for improved patient outcomes.