Malek Ahmad Alsoud
Digital Marketing Department / University of Petra, Jordan
Suleiman Mohammad
Department of Business Administration, Business School, Al al-Bayt University, Mafraq 25113, Jordan; Research follower, INTI International University, 71800 Negeri Sembilan, Malaysia
Asokan Vasudevan
Faculty of Business and Communications, INTI International University, 71800 Negeri Sembilan, Malaysia
Suhaila Abuowaida
Department of Data Science and Artificial Intelligence, Faculty of Prince Al-Hussein Bin Abdallah II for IT, Al al-Bayt University. Mafraq, Jordan
Huiying Jiang
Faculty of Liberal Arts, Shinawatra University, Thailand
Mohammad Hunitie
Department of Public Administration, School of Business, University of Jordan, Jordan
The increasing adoption of artificial intelligence in healthcare has significantly improved predictive analytics and clinical decision-support capabilities; however, challenges related to interpretability, adaptability, and trustworthiness continue to limit widespread clinical deployment. This study proposes an Explainable Dynamical Clinical Intelligence Architecture (EDCIA) that integrates nonlinear dynamical systems modeling, explainable artificial intelligence, temporal learning, causal interaction analysis, graph-based healthcare modeling, reinforcement-driven optimization, and Bayesian uncertainty forecasting within a unified framework. The proposed architecture enables transparent disease-risk prediction, patient-state monitoring, treatment optimization, and adaptive clinical decision-making. By combining predictive accuracy with explainability and uncertainty awareness, the framework provides reliable and interpretable support for complex healthcare environments.
