Artificial Intelligence in Early Diagnosis of Non-Communicable Diseases: Innovations and Challenges

Abstract

Non-communicable diseases (NCDs), including cardiovascular diseases, cancer, diabetes, and chronic respiratory illnesses, are the leading causes of morbidity and mortality worldwide. Early diagnosis plays a critical role in improving patient outcomes and reducing healthcare costs. Artificial intelligence (AI) has emerged as a transformative tool in medical diagnostics, offering enhanced accuracy, efficiency, and predictive capabilities. This study explores the innovations and challenges associated with AI-driven early diagnosis of NCDs, focusing on machine learning algorithms, deep learning models, and big data analytics. AI-powered imaging techniques, such as convolutional neural networks (CNNs), have demonstrated remarkable success in detecting early-stage cancer, cardiovascular abnormalities, and diabetic retinopathy with high precision. Additionally, AI-integrated electronic health records (EHRs) facilitate risk assessment by analyzing patient history and identifying disease patterns, enabling proactive interventions.