Exploring The Role of Artificial Intelligence in Early Diagnosis of Diabetes

Increasing accuracy and efficiency together with diagnosis predictability makes artificial intelligence (AI) revolutionize healthcare sector operations. This paper explores how artificial intelligence (AI) detects early-stage diabetes mellitus as a metabolic chronic disease that shows increasing global prevalence. The objective of this research study evaluates how different artificial intelligence approaches including machine learning, deep learning, and natural language processing (NLP) function in terms of methods, accuracy level as well as data collection protocols and their practical clinical benefits. Research indicates that ensemble machine learning models alongside convolutional neural networks (CNNs) demonstrate superior identification abilities for detecting diabetes problems early on. The data quality issues along with generalization problems and limitations for clinical practice require further investigation.