Artificial Intelligence in Healthcare Data Processing and Analysis: Standardization, Mining, and Privacy Protection

Abstract

With the rapid digitization of healthcare systems, the volume of healthcare data (e.g., electronic health records (EHRs), medical images, and genomic data) has grown exponentially, creating both opportunities and challenges for medical research and clinical practice. Artificial Intelligence (AI) has emerged as a pivotal tool to address key issues in healthcare data management, including data heterogeneity, low usability, and privacy risks. This paper systematically explores three core dimensions of AI-enabled healthcare data processing and analysis: first, technologies for healthcare data standardization and structuring, focusing on data cleaning algorithms and structured modeling approaches for EHRs to improve data quality and interoperability; second, AI-driven healthcare data mining techniques, emphasizing the extraction of disease-related features from multi-source heterogeneous data and the development of machine learning models for risk factor prediction; third, privacy protection and security technologies for healthcare big data, with a particular focus on the applications of federated learning and differential privacy in enabling secure data sharing without compromising patient confidentiality. Through a comprehensive review of existing literature, case studies, and technical frameworks, this paper identifies current challenges in the field (e.g., model interpretability, data quality inconsistencies) and proposes future research directions to advance the practical application of AI in healthcare data management. The findings of this study aim to provide valuable insights for researchers, clinicians, and policymakers in promoting the safe, efficient, and ethical use of AI for healthcare data analytics.