AI-Enhanced Blood Testing for Disease Detection and Monitoring

This paper presents a survey of artificial intelligence (AI) applications in blood testing for disease detection and patient monitoring. It covers several machine learning (ML) models like deep neural networks (DNN), decision trees, and support vector machines (SVM) used to interpret blood test results. The integration of AI with routine blood tests promises to enhance the diagnostic accuracy, reduce costs, and also improve patient outcomes. This paper compares different AI approaches in this domain, discusses the challenges and limitations, and explores the future scope of AI in clinical settings.AI in Healthcare, Blood Test Analysis, Machine Learning, Disease Detection, Deep Learning, Clinical Diagnostics.