Enhancing Diagnostic Accuracy Using Artificial Intelligence and Convolutional Neural Networks

Abstract

Accurate and timely medical diagnosis plays a critical role in effective healthcare delivery. Traditional diagnostic methods often depend heavily on expert interpretation, which can be time-consuming and prone to variability. Recent advancements in Artificial Intelligence (AI), particularly Convolutional Neural Networks (CNNs), have proved substantial potential in automating and improving diagnostic accuracy using medical imaging data. This paper presents an AI-driven CNN-based framework designed to enhance diagnostic precision by extracting high-level features from medical images. The proposed approach improves classification performance while reducing human dependency. Experimental evaluation on benchmark medical imaging datasets demonstrates that the CNN model achieves superior accuracy, sensitivity, and reliability compared to conventional methods. The results highlight the effectiveness of CNN-based AI systems as supportive tools for medical professionals in clinical decision-making.