A Governance-Constrained Framework for Adaptive Bio-Cyber-Physical Systems in Medicine

This work introduces a governance-constrained conceptual and architectural framework for adaptive bio-cyber-physical systems in medicine. Contemporary clinical medicine operates primarily through episodic and reactive paradigms that are structurally misaligned with the continuous and dynamical nature of biological systems. This paper formalizes disease progression as a dynamical system evolving within a multidimensional biological state space defined by interacting inflammatory, vascular, metabolic, mechanical, and genetic variables. The proposed framework integrates biological sensing, computational state estimation, predictive modelling, and controlled intervention within a governance-constrained architecture. Artificial intelligence operates strictly within bounded decision-support roles under explicit safety and uncertainty constraints, preserving human clinical authority at all times. This work establishes a scientific and methodological foundation for the future development of adaptive precision medicine systems and governance-constrained bio-cyber-physical medical infrastructures.