Towards AI-Driven Standardization in Disease Indication: Implementing Controlled Vocabulary for Clinical Reporting Systems

Abstract

The standardization of disease indications in clinical reporting systems using AI-driven approaches is examined in this research. The evaluation assesses the accuracy, efficiency, scalability, and clinical usefulness of NLP approaches such as Named Entity Recognition (NER), Entity Linking, Supervised Machine Learning (SVM), Unsupervised Machine Learning (K-means), and Ontology-Based Approaches. The study emphasizes the advantages of each technique and its function in representing structured clinical data.