Systemic Information Ontology of the Living: A Missing Foundation for Next-Generation Medical AI

Modern medical AI systems operate within a natural-philosophical paradigm that renders them structurally incapable of modeling the fundamental organization of living systems. This is not a computational limitation – it is an ontological one. Neither increased processing power nor quantum computing can resolve what is inherently a problem of model architecture. The author presents the Systemic Information Ontology of the Living (SIOL) – a two-level, recursively organized framework that formally describes diseases not as pathological deviations but as natural phenomena of the living: programs of quantum dimension stored in phylogenetic memory, manifesting through Newtonian-dimension pathogenesis patterns. SIOL provides what current medical AI lacks: a formal distinction between causality and correlation, a binary classification system of diseases with verifiable structural relationships, and, crucially, diagnostic classification tables enabling five-year disease prognosis with a verified probability of no less than 70%. The commercial potential of this ontology for next-generation medical AI development is substantial and currently unrealized.