Co-Lifecycle Governance for Learning Medical AI: A Hybrid Convergence Framework for Adaptive Regulatory Oversight.

Area of Science:

• Healthcare Regulation
• Artificial Intelligence
• Medical Technology Governance

Background:

• Adaptive artificial intelligence (AI) in healthcare presents challenges to static regulatory frameworks.
• AI model evolution (drift, recalibration, degradation) creates mismatches with traditional oversight.
• Existing regulatory structures struggle with the dynamic nature of learning AI systems, leading to responsibility gaps.

Purpose of the Study:

• To propose Co-Lifecycle Governance as a conceptual framework for regulating adaptive AI in healthcare.
• To synchronize regulatory oversight with the continuous evolution of AI systems.
• To address the structural mismatch between learning AI and static regulatory approaches.

Main Methods:

• Analysis of current regulatory responses in the US, EU, and South Korea.
• Conceptualization of Co-Lifecycle Governance based on continuous validation, agile change management, proactive performance surveillance, and distributed accountability.
• Proposal of a hybrid convergence pathway synthesizing international regulatory strengths.

Main Results:

• Learning AI necessitates governance that moves beyond static rules and episodic evaluation.
• Co-Lifecycle Governance offers a continuous, synchronized regulatory process to manage AI evolution.
• International regulatory trajectories show convergence on the need for adaptive oversight mechanisms.

Conclusions:

• Co-Lifecycle Governance provides a foundation for regulating AI that learns and evolves.
• A hybrid approach combining US predictability, EU accountability, and Korean scalability is proposed.
• Regulatory oversight must evolve in lockstep with adaptive AI to ensure safety and maintain trust.