Thesis Abstract

Abstract
Artificial intelligence (AI) has revolutionized various sectors, including healthcare. In radiography, AI has shown great potential in assisting with image interpretation, thereby impacting clinical practice. This thesis aims to assess the impact of AI on radiographic image interpretation in clinical practice. The study delves into the background of AI in radiography, the problem statement surrounding its implementation, the objectives of the study, limitations encountered, scope of the study, significance of the research, structure of the thesis, and key definitions of terms. The literature review examines ten key aspects related to AI in radiography, including the evolution of AI in healthcare, current trends in AI applications in radiography, challenges faced in AI implementation, and ethical considerations in utilizing AI for image interpretation. The research methodology section outlines the approach taken in conducting this study, covering aspects such as research design, data collection methods, participant selection criteria, data analysis techniques, and potential biases that may have influenced the results. Additionally, it discusses the tools used for image analysis and AI algorithms incorporated in the study. The discussion of findings presents a detailed analysis of the impact of AI on radiographic image interpretation in clinical practice. It explores the accuracy, efficiency, and reliability of AI algorithms compared to traditional methods, as well as the potential benefits and challenges associated with AI integration into routine practice. Furthermore, it highlights the perspectives of radiographers, radiologists, and other healthcare professionals on the adoption of AI in radiography. The conclusion and summary section encapsulate the key findings of the study, emphasizing the significance of AI in enhancing radiographic image interpretation and its implications for future clinical practice. It also discusses the limitations of the study and offers recommendations for further research and practical implementation of AI in radiography. In conclusion, this thesis contributes to the growing body of knowledge on the impact of AI in radiography and provides insights into how AI can improve the efficiency and accuracy of radiographic image interpretation in clinical settings. The findings of this study have implications for healthcare professionals, policymakers, and researchers looking to leverage AI technology to enhance patient care and diagnostic outcomes in radiology.

Thesis Overview

The project titled "Assessment of the Impact of Artificial Intelligence on Radiographic Image Interpretation in Clinical Practice" aims to investigate the influence of artificial intelligence (AI) on the interpretation of radiographic images in the field of clinical practice. In recent years, AI technologies have rapidly advanced and shown great potential in various industries, including healthcare. This study specifically focuses on how AI tools and algorithms can enhance the accuracy, efficiency, and overall quality of radiographic image interpretation in clinical settings. The research will begin with a comprehensive literature review to explore the current state of AI applications in radiography and image interpretation. This review will cover key concepts such as machine learning, deep learning, and neural networks, as well as relevant studies and developments in the field. By examining existing research and technologies, the study aims to provide a solid foundation for understanding the potential impact of AI on radiographic image interpretation. Following the literature review, the research methodology will be outlined, detailing the approach, data collection methods, and analysis techniques to be employed. The study will utilize both quantitative and qualitative data to assess the effectiveness of AI tools in improving radiographic image interpretation accuracy and efficiency. This may involve conducting experiments, surveys, or interviews with radiographers, clinicians, and other healthcare professionals to gather insights and feedback on the use of AI in clinical practice. The core of the study will involve analyzing the findings from the research, with a focus on identifying the benefits and challenges associated with integrating AI into radiographic image interpretation. Factors such as accuracy, speed, cost-effectiveness, and user experience will be evaluated to determine the overall impact of AI technologies on clinical practice. The discussion of findings will also explore potential implications for healthcare providers, patients, and the broader healthcare system. In conclusion, the study will summarize the key findings, implications, and recommendations for future research and practice. By assessing the impact of artificial intelligence on radiographic image interpretation in clinical practice, this project aims to contribute to the growing body of knowledge on AI applications in healthcare and provide valuable insights for improving diagnostic processes and patient care.