Artificial Intelligence-Enabled Health Information Technology Systems for Clinical Decision Support and Healthcare Administration: A Systematic Review of Adoption, Data Quality, Trust, and Ethical Challenges

Artificial intelligence is increasingly being embedded within Health Information Systems to support clinical decision-making, hospital administration, patient monitoring, workflow coordination, and resource allocation. However, the value of AI-enabled Health Information Systems depends not only on algorithmic performance, but also on adoption readiness, data quality, trust, ethical governance, privacy protection, and accountability. This systematic review examined existing evidence on the applications, benefits, challenges, and responsible implementation requirements of AI-enabled Health Information Systems for clinical decision support and healthcare administration. The review followed a PRISMA-guided methodology and synthesized peer-reviewed studies published between 2015 and 2026 from major health, computing, and information systems databases. The findings show that AI-enabled systems are widely applied in diagnosis, triage, risk prediction, electronic health records, clinical alerts, medical imaging, documentation support, and operational management. Evidence also indicates that these systems can improve decision speed, accuracy, service efficiency, workflow planning, and administrative coordination. Nevertheless, implementation remains constrained by fragmented data, weak interoperability, limited external validation, clinician uncertainty, patient privacy concerns, algorithmic bias, explainability gaps, regulatory ambiguity, and insufficient institutional preparedness. The review further reveals that many studies still focus on model performance while giving less attention to real-world integration, monitoring, governance, and user trust. The study, therefore, proposes a socio-technical understanding of AI-enabled Health Information Systems, where reliable data, transparent algorithms, human oversight, ethical safeguards, organizational readiness, and continuous evaluation are treated as essential conditions for responsible adoption. The review contributes a practical foundation for trustworthy, effective, and ethically governed AI integration in healthcare systems and policy planning.