Application of Artificial Intelligence in Radiography for Improved Diagnostic Accuracy

Thesis Abstract

Abstract
This thesis explores the application of artificial intelligence (AI) in radiography as a means to enhance diagnostic accuracy in medical imaging. The rapid advancements in AI technologies have opened up new possibilities for improving healthcare outcomes, particularly in the field of radiology. The integration of AI algorithms in radiography has the potential to streamline image analysis, reduce interpretation errors, and ultimately enhance the quality of patient care. The research methodology involved in-depth literature review to understand the current landscape of AI applications in radiography. Various AI techniques such as machine learning, deep learning, and computer vision were examined for their potential to assist radiologists in image interpretation and diagnosis. The literature review also highlighted the challenges and limitations associated with AI implementation in radiography, including issues related to data privacy, algorithm bias, and lack of standardized protocols. The findings from this study suggest that AI can significantly improve diagnostic accuracy in radiography by assisting radiologists in detecting abnormalities, quantifying disease severity, and predicting patient outcomes. AI-powered tools like computer-aided detection systems and automated image segmentation algorithms have shown promising results in various radiological applications, including detection of tumors, assessment of bone fractures, and classification of lung diseases. The discussion of findings delves into the practical implications of integrating AI in radiography, including the impact on radiology workflow, radiologist-patient interactions, and overall healthcare costs. The potential benefits of AI in radiography, such as faster image analysis, reduced interpretation errors, and improved patient outcomes, are weighed against the challenges of algorithm interpretability, regulatory compliance, and ethical considerations. In conclusion, the study emphasizes the importance of a collaborative approach between radiologists and AI systems to harness the full potential of AI in radiography. While AI technologies offer exciting opportunities to enhance diagnostic accuracy and efficiency in medical imaging, it is crucial to address the ethical, legal, and social implications of AI integration in healthcare. Future research directions include exploring novel AI techniques, optimizing AI model performance, and developing guidelines for responsible AI deployment in radiography. Overall, this thesis contributes to the growing body of knowledge on the application of artificial intelligence in radiography and provides insights into the potential benefits and challenges of incorporating AI technologies in clinical practice. By leveraging AI capabilities to augment radiologist expertise, the field of radiography can strive towards improved diagnostic accuracy, enhanced patient care, and ultimately, better healthcare outcomes.