AUTOMATING ANALYSIS WORKFLOWS WITH AI: TOOLS FOR STREAMLINED DATA UPLOAD AND REVIEW IN CLINICAL SYSTEMS

Abstract

The present research looks into the automation of clinical analytic procedures in healthcare systems using support vector machines (SVMs), convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The study intends to enhance computational efficiency, prediction accuracy, and interpretability in clinical data processing activities by conducting a thorough investigation of technical capabilities, implementation approaches, and performance indicators. The feasibility of each algorithm for certain healthcare applications is determined by examining key features such as model scalability, training duration, computational resource utilization, and handling of varied data formats. The results contribute to the progress of AI-driven automation in clinical environments, by tackling obstacles and laying the foundation for improved healthcare provision and patient results.