Artificial intelligence methods and approaches to improve data quality in helthcare data

Abstract

This study explores artificial intelligence (AI) methods and approaches used to improve data quality, with a particular focus on healthcare data. Applying a systematic literature review based on the PRISMA framework, the research examines publications from 2020 to 2025 that analyze AI applications across key data quality dimensions—accuracy, completeness, consistency, timeliness, uniqueness, and validity. The study aims to identify which AI methods are most commonly employed and how they align with these quality attributes. A conceptual map was developed to visualize the relationships between dimensions and AI techniques such as deep learning, federated learning, data-centric AI, and ontology-based data governance. Findings reveal that accuracy and consistency are the most emphasized dimensions in the literature, with methods like supervised learning, NLP, and isolation forest frequently applied. In contrast, dimensions like timeliness and validity receive comparatively limited attention. The study concludes that certain AI methods—particularly data-centric and cross-cutting approaches—are effective in addressing multiple data quality challenges simultaneously. These insights offer practical guidance for selecting AI strategies in healthcare data quality improvement and highlight areas for future research.