AI-ENABLED MEDICAL IMAGING AND DEEP LEARNING TECHNIQUES FOR IMPROVED DIAGNOSIS AND TREATMENT PLANNING

Abstract

The integration of artificial intelligence (AI) and deep learning into medical imaging has revolutionized diagnostic processes and treatment planning in modern healthcare systems. Traditional imaging analysis methods rely heavily on human expertise, which can be time-consuming and prone to variability. AI-enabled systems, particularly those based on deep learning architectures such as convolutional neural networks (CNNs), have demonstrated significant potential in automating image interpretation, improving diagnostic accuracy, and supporting clinical decision-making. This study presents a comprehensive analytical framework for evaluating AI-driven medical imaging systems, focusing on their application in disease detection, image segmentation, and predictive analytics for treatment planning. The research adopts a hybrid methodological approach integrating image processing, model training, and performance evaluation to assess the effectiveness of deep learning techniques. The findings indicate that AI-based imaging systems significantly enhance diagnostic precision, reduce processing time, and enable personalized treatment strategies. However, challenges related to data quality, model interpretability, and ethical considerations remain critical. The study contributes to the advancement of intelligent healthcare systems by providing a structured framework for integrating AI technologies into clinical workflows.