A Proposed Framework for Prediction Processes in AI-enhanced Virtual Environments in the Administrative and Medical Fields

Abstract

Traditional predictive models in the administrative and medical fields face limitations in dealing with complex dynamic scenarios and predicting unprecedented crises, due to their heavy reliance on historical data. This research bridges the scientific gap by proposing a forward-looking theoretical framework based on developing intelligent three-dimensional virtual environments that simulate reality with high accuracy, processing big data (medical and administrative) as testable prototypes through artificial intelligence analytics. These environments enable the simulation of multiple future scenarios (including "black swan" events), thereby reducing risks and uncertainty, and producing accurate predictions that support critical administrative decisions and track patient health progression. The research presents six research proposals (RPs) covering both the administrative domain (developing proactive systems for human resource management and predicting future skill gaps) and the medical domain (personalizing treatment plans and predicting recovery trajectories). The research also discusses the ethical and technical challenges associated with deploying these systems, such as data privacy and algorithmic biases. The study contributes to a qualitative shift from traditional reactive forecasting to a proactive simulation-based approach, thereby enhancing future planning and crisis preparedness capabilities in both the administrative and medical fields.