Background The expanding application of artificial intelligence (AI) in health information systems has created new opportunities for improving diagnosis, predicting clinical outcomes, managing healthcare resources, and enabling personalized care. However, the dependence of AI models on large volumes of health data has introduced substantial challenges related to data governance, privacy, security, and the responsible use of patient information. The lack of harmonized frameworks for managing the data lifecycle, limited algorithmic transparency, and concerns regarding data ownership and sharing represent major barriers to the widespread adoption of AI in clinical settings. Objective This systematic review aimed to identify the key dimensions of data governance, privacy challenges, and data protection approaches in artificial intelligence-based health information systems. Methods A systematic review was conducted focusing on studies published between January ۲۰۲۰ and August ۲۰۲۶. Studies related to healthcare AI, data governance, health information privacy, data security, and privacy-preserving technologies were evaluated. The included sources consisted of systematic reviews, comprehensive reviews, international policy frameworks, and relevant research articles. Data were extracted using a thematic analysis approach and categorized according to the main dimensions of data governance. Results The analysis indicated that data governance in healthcare AI encompasses several interconnected domains, including privacy protection, data security, data quality and integrity, access control, algorithmic transparency, accountability, and regulatory compliance. The main identified challenges included the risk of patient information disclosure, attacks against artificial intelligence models, lack of common data exchange standards, limited transparency of algorithmic decisions, and uncertainty regarding legal responsibility. Technologies such as federated learning, differential privacy, homomorphic encryption, and blockchain were identified as key approaches for enhancing data security and confidentiality. Conclusion Successful implementation of artificial intelligence in health information systems requires a multilayered data governance approach that simultaneously addresses technical, ethical, legal, and organizational requirements. Developing integrated AI governance frameworks can contribute to building trust, protecting patient rights, and facilitating the responsible use of health data.
