Anber Abraheem Shlash Mohammad
Digital Marketing Department, Faculty of Administrative and Financial Sciences, University of Petra, Jordan
Suleiman Ibrahim 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
Jin Zhang
Faculty of Liberal Arts, Shinawatra University, Thailand
Mohammad Faleh Hunitie
Department of Public Administration, School of Business, University of Jordan, Jordan
This study proposes a novel Multi-Scale Personalized Intelligence Architecture (MSPIA) for adaptive personalized medicine using artificial intelligence and mathematical systems modeling. The framework integrates nonlinear biological progression equations, transformer-based multi-omics learning, graph neural molecular interaction analysis, Bayesian uncertainty forecasting, and reinforcement-learning-driven therapeutic optimization within a unified computational ecosystem. The proposed methodology captures hierarchical interactions among molecular, cellular, physiological, and clinical healthcare dynamics to generate patient-specific therapeutic strategies. Experimental evaluation demonstrated superior predictive accuracy, improved survival forecasting, enhanced treatment optimization, and reduced adverse therapeutic effects. The findings confirm that the proposed framework provides a scalable and intelligent solution for next-generation personalized medicine systems.
