DEVELOPMENT OF AN AUTOMATED DIAGNOSTIC SYSTEM USING RADIOLOGY DATA

Abstract

Automated diagnostic systems utilizing radiology data have the potential to revolutionize medical imaging by enhancing diagnostic accuracy, reducing human error, and streamlining clinical workflows. Artificial intelligence (AI) and machine learning (ML) technologies enable the development of systems that can analyze complex imaging datasets, identify abnormalities, and provide decision support for radiologists. This paper presents an overview of automated diagnostic systems based on radiology data, focusing on methodologies such as deep learning, convolutional neural networks, and hybrid AI models. Performance metrics, clinical applicability, challenges including data variability and integration into healthcare workflows, and future perspectives are discussed. The study emphasizes the transformative role of AI in improving diagnostic efficiency and supporting evidence-based clinical decision-making.