Named Entity Recognition for AI-Driven Medical Text Processing in the Silicon Revolution

The rapid advancement of artificial intelligence (AI) in healthcare has revolutionized medical text processing, particularly in clinical decision-making, diagnostics, and patient management. Named Entity Recognition (NER), a fundamental task in natural language processing (NLP), plays a crucial role in extracting critical medical entities such as diseases, symptoms, medications, and procedures from unstructured clinical text. Traditional NER models, including rule-based and classical machine learning approaches, often struggle with domain-specific terminology, contextual ambiguity, and limited generalization across diverse medical datasets. These limitations hinder the accurate identification and extraction of medical entities, reducing the effectiveness of AI-driven healthcare solutions. To address these challenges, we propose a novel AI-powered NER framework that integrates domain-adaptive clinical optimization, multi-modal fusion, and uncertainty-aware prediction mechanisms. Our method leverages a Clinical-Aware Neural Network (CANN), which enhances entity recognition by incorporating structured medical knowledge graphs, hierarchical feature extraction, and adversarial training to improve robustness against data heterogeneity. We introduce a Domain-Adaptive Clinical Optimization (DACO) strategy, ensuring model generalization across diverse patient populations while mitigating class imbalance and noise in medical text. Experimental results demonstrate that our proposed approach significantly outperforms conventional NER methods in precision, recall, and F1-score, particularly in complex medical narratives. The integration of AI-driven techniques into medical text processing not only enhances clinical information retrieval but also ensures reliable and interpretable entity recognition, paving the way for more intelligent and ethical AI applications in healthcare.