Co-Lifecycle Governance for Learning Medical AI: A Hybrid Convergence Framework for Adaptive Regulatory Oversight
Clinical Snapshot
PICO Framework
| P — Population | Healthcare systems implementing adaptive AI technologies across US, EU, and South Korea |
| I — Intervention | Co-Lifecycle Governance framework with four pillars: continuous validation, agile change management, proactive performance surveillance, and distributed accountability |
| C — Comparator | Current static regulatory frameworks and episodic evaluation systems |
| O — Outcomes | Regulatory synchronization with adaptive AI, improved safety oversight, maintained system trust, and operational feasibility |
Bottom Line
This viewpoint proposes a Co-Lifecycle Governance framework to address the regulatory challenges posed by adaptive medical AI systems that evolve continuously. The authors argue that current static regulatory approaches are inadequate for AI systems that change through real-world deployment. Their four-pillar framework emphasizes continuous validation, agile change management, proactive surveillance, and distributed accountability. While the conceptual framework addresses an important regulatory gap and synthesizes international approaches, it lacks empirical validation and implementation details. For Australian healthcare, this work provides valuable insights for TGA's AI regulation development, particularly as adaptive AI systems become more prevalent. However, the framework requires practical testing and stakeholder engagement before adoption. The paper's strength lies in identifying the temporal mismatch between AI evolution and regulatory oversight, but its weakness is the absence of evidence supporting the proposed solution's effectiveness.
Key Findings
Effect Size: Not applicable - conceptual framework
Primary Outcome: Proposed Co-Lifecycle Governance framework with four pillars for adaptive AI oversight
Nnt Or Sensitivity: Framework applicability across US, EU, and Korean regulatory contexts
Clinical Application
Theoretical framework requires empirical testing and stakeholder validation before implementation Relevant to TGA's evolving AI regulation approach; could inform Australian AI governance development alongside international frameworks Healthcare organizations implementing adaptive AI systems, regulatory bodies overseeing medical AI
Abstract
Artificial intelligence (AI) in health care is increasingly defined not by static algorithms but by adaptive intelligence-systems that evolve over time through interactions with data, clinicians, and clinical environments. This adaptive capacity creates a structural mismatch with regulatory frameworks built for technologies whose behavior remains static. As AI models drift, recalibrate, or degrade in real-world contexts, they dissolve the linear boundaries between design, deployment, and clinical interpretation. These temporal, epistemic, and organizational frictions expose responsibility gaps that cannot be resolved through incremental modifications to legacy oversight structures. Regulators across major jurisdictions are beginning to respond to these challenges, though with differing orientations. The United States advances mechanisms for predictable adaptation, including Predetermined Change Control Plans, real-world evidence frameworks, and life cycle-oriented quality management reforms. The European Union emphasizes precautionary, rights-based governance through the European Union Artificial Intelligence Act (AI Act) and modernized liability rules. South Korea, operating within a hyperconnected digital health ecosystem, has introduced the Digital Medical Products Act (DMPA), one of the world's first comprehensive statutory frameworks for learning medical AI. Despite philosophical differences, these regulatory trajectories converge on a shared insight: learning AI systems cannot be governed by static rules or episodic evaluation. This viewpoint proposes Co-Lifecycle Governance as a conceptual framework to synchronize regulatory oversight with adaptive intelligence. Rather than treating oversight as a discrete event, Co-Lifecycle Governance frames regulation as a continuous, synchronized process grounded in 4 pillars: continuous validation, agile change management, proactive performance surveillance, and distributed accountability. Each pillar functions as a structural antidote to the responsibility frictions that arise when AI systems evolve faster than expectations surrounding them. Together, these pillars provide a governance grammar capable of supporting safe, iterative model improvement while maintaining system-level trust. Drawing from the strengths of US predictability, European Union accountability, and Korean scalability, this paper outlines a hybrid convergence pathway that synthesizes predictability, accountability, and operational feasibility. Learning AI will not wait for governance to catch up; oversight must evolve in lockstep with adaptive intelligence. Co-Lifecycle Governance offers a foundation for regulatory systems that not only regulate learning AI but also learn with it-at the speed at which adaptive intelligence actually changes.
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