Thesis Abstract

Abstract
The rapid advancements in Artificial Intelligence (AI) have led to its integration into various fields, including healthcare. Radiography, as a crucial diagnostic tool, stands to benefit significantly from the application of AI technologies. This thesis explores the potential of AI in radiography for improving the accuracy and efficiency of diagnostic processes. The research aims to investigate how AI can enhance the interpretation of radiographic images, leading to more precise and timely diagnoses. Chapter 1 provides an introduction to the study, presenting the background of the research, the problem statement, research objectives, limitations, scope, significance, structure of the thesis, and the definition of key terms. The literature review in Chapter 2 examines existing studies on the application of AI in radiography, highlighting key findings and gaps in knowledge. Chapter 3 outlines the research methodology, including the research design, data collection methods, sample selection, data analysis techniques, and ethical considerations. The chapter also discusses the challenges and limitations faced during the research process. In Chapter 4, the findings of the study are presented and analyzed in detail. The results shed light on how AI technologies can improve the accuracy and efficiency of radiographic diagnosis, offering insights into the practical implications of integrating AI tools into radiography practice. Finally, Chapter 5 concludes the thesis by summarizing the key findings, discussing their implications for radiography practice, and offering recommendations for future research and implementation. The study underscores the transformative potential of AI in radiography and its ability to enhance diagnostic outcomes, thereby contributing to improved patient care and healthcare efficiency. In conclusion, the research on the "Application of Artificial Intelligence in Radiography for Improved Diagnosis" underscores the importance of leveraging AI technologies to enhance diagnostic accuracy and efficiency in radiography practice. By harnessing the power of AI, radiographers and healthcare providers can provide more precise and timely diagnoses, ultimately benefiting patients and advancing the field of radiography.

Thesis Overview

The project titled "Application of Artificial Intelligence in Radiography for Improved Diagnosis" delves into the integration of cutting-edge technology to enhance diagnostic capabilities in the field of radiography. Artificial intelligence (AI) has emerged as a powerful tool in various industries, including healthcare, revolutionizing the way medical data is processed and interpreted. In the context of radiography, AI has the potential to significantly impact the accuracy and efficiency of diagnostic procedures, ultimately leading to improved patient outcomes. This research overview aims to explore the utilization of AI algorithms in radiography to streamline the diagnostic process and provide healthcare professionals with more accurate and timely information for treatment planning. By leveraging the capabilities of AI, radiographers can analyze large volumes of medical images with greater precision and speed, aiding in the early detection and diagnosis of a wide range of medical conditions. The study will encompass a comprehensive literature review to examine existing research and developments in the field of AI in radiography. This will involve exploring the various AI techniques and algorithms that have been applied to medical imaging, as well as the challenges and opportunities associated with integrating AI into clinical practice. Furthermore, the research methodology will involve the implementation of AI models on a dataset of medical images to evaluate their performance in diagnosing common medical conditions. By comparing the results obtained from AI-assisted diagnosis with those from traditional methods, the study aims to showcase the potential benefits of AI in improving diagnostic accuracy and efficiency in radiography. The discussion of findings will present a detailed analysis of the outcomes obtained from the AI models, highlighting their strengths and limitations in real-world clinical settings. Additionally, the study will address the ethical considerations and regulatory requirements associated with the adoption of AI in healthcare, emphasizing the importance of patient privacy and data security. In conclusion, this research project seeks to contribute to the growing body of knowledge on the application of AI in radiography for improved diagnosis. By harnessing the power of AI technology, healthcare professionals can enhance their diagnostic capabilities, leading to more precise and personalized treatment options for patients. The findings of this study are expected to have significant implications for the future of radiography practice, paving the way for more efficient and effective healthcare delivery.