Published Paper Details:

Abstract: This article explores the role of artificial intelligence (AI) in medical imaging interpretation, focusing on its applications in radiology. Radiological image interpretation is fundamental to medical diagnostics, aiding in the detection, diagnosis, and management of various diseases. Traditional methods rely on human intuition, but the complexity of images can lead to errors and variability in outcomes. The evolution of AI in medical imaging has been significant, with advancements in technology and methodology over several decades. Current technologies, such as deep learning algorithms like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), offer solutions to longstanding challenges in image interpretation. Key studies demonstrate the transformative potential of AI in detecting abnormalities, segmenting anatomical structures, and aiding in treatment planning. However, challenges persist, including the need for standardization in data organization, policy considerations for data sharing, and concerns regarding model explainability and privacy. Achieving true "intelligence" at the human level remains a key consideration for the future of AI in medical imaging. Overall, this research highlights the critical importance of AI in advancing radiological interpretation, offering opportunities to improve diagnostic accuracy, efficiency, and patient outcomes.