AI-BASED EARLY DISEASE DETECTION USING MEDICAL IMAGING

Abstract

Timely detection of diseases increases the chances of effective treatment and lowers the costs of care. Artificial Intelligence (AI), specifically deep learning and computer vision, has been a rapidly evolving and promising method that has the potential to transform early stage disease detection in the context of medical imaging. This research paper provides a comprehensive review of AI-based diagnostic tools with an emphasis on the use of convolutional neural networks (CNNs) and other deep learning platforms in the diagnosis of radiology. Although this paper will review all imaging modalities that include CT, MRI, X-ray, and ultrasound, we will highlight how AI models have been used in diagnosing early diseases, including cancer, pneumonia, Alzheimer's disease, and cardiovascular illness. We review many recent studies and highlight our experimental results using publicly available datasets, while discussing opportunities, limitations, and future perspectives. We conclude that AI can increase accuracy, reduce the radiology workload, and allow for healthcare equity and access especially in underserved areas.