In this paper, the authors discuss the use of AI technologies in decision-making processes within hospital management. These technologies include resource allocation, predictive analytics, patient flow optimization, and diagnostics, each of which contributes to a streamlined, data-informed approach to hospital management. 

Through detailed case studies, the authors analyze the impact of tools such as machine learning and natural language processing on key administrative tasks—highlighting, for instance, how AI can accurately forecast patient admissions, automate complex scheduling processes, and reduce overhead costs. Such capabilities allow healthcare systems to manage demand spikes and resource allocation with unprecedented agility, building resilience across hospital networks.

The study also examines the implementation challenges of AI, including data privacy concerns, the need for interoperability across digital platforms, and the necessity of continuous model validation to ensure accuracy and ethical compliance. The authors emphasize the importance of building an ethical, transparent framework for AI use to foster trust among healthcare stakeholders and ensure compliance with regulatory standards.