Artificial Intelligence in Healthcare Administration and Clinical Informatics: A Critical Review and Governance Roadmap

Artificial intelligence (AI) is increasingly influencing healthcare administration and clinical informatics by supporting disease diagnosis, clinical decision-making, treatment personalization, drug discovery, remote monitoring, public health surveillance, and hospital operations. However, the successful adoption of AI in healthcare depends not only on algorithmic performance, but also on its safe integration into clinical information systems, organizational workflows, and governance structures. This article presents a narrative critical review of recent advances in AI-driven healthcare, with a focus on four major domains: AI-enabled disease diagnosis, treatment personalization and clinical decision support, drug discovery and biomedical knowledge generation, and healthcare administration. Evidence from radiology, pathology, ophthalmology, dermatology, and cardiology shows that AI systems can achieve strong diagnostic performance in selected settings, while applications in electronic health records, natural language processing, telemedicine, and predictive analytics are increasingly used to support healthcare delivery and operational decision-making. At the same time, important barriers continue to limit real-world implementation, including fragmented data infrastructures, limited interoperability, poor data quality, algorithmic bias, lack of explainability, privacy and cybersecurity risks, unclear accountability, and insufficient external validation. This review critically examines these challenges and proposes a governance-oriented roadmap for responsible AI integration in healthcare administration and clinical informatics. The proposed roadmap emphasizes data readiness, model validation, workflow integration, institutional accountability, post-deployment monitoring, and workforce readiness. The findings suggest that AI can contribute to more efficient, accessible, and patient-centered healthcare only when it is implemented within trustworthy medical informatics ecosystems supported by ethical governance, human oversight, and continuous evaluation.