To introduce the Futures Framework for Clinical Artificial Intelligence Governance (FF-CAIG) that integrates futures methodologies with clinical AI governance.
Approach:
Futures Framework for Clinical AI Governance (FF-CAIG): FF-CAIG is grounded in core futures methodologies and focuses on patient safety and health equity, addressing emerging systemic and clinical risks, evolving stakeholder roles, and regulatory responses.
Key Findings:
Current clinical AI governance primarily focuses on foreseeable changes and measurable postdeployment signals.
Existing oversight models are limited in addressing long-term governance challenges posed by evolving clinical AI technologies.
FF-CAIG offers a structured approach to anticipate and manage risks associated with clinical AI deployment.
Interpretation:
The governance of clinical AI requires a shift from traditional oversight models to more anticipatory frameworks that can adapt to rapid technological changes and emerging risks.
Limitations:
FF-CAIG is not intended for low-impact administrative automation.
The framework may not fully capture all emerging risks due to the unpredictable nature of technological advancements.
Conclusion:
FF-CAIG provides a conceptual and practical tool for stakeholders in clinical AI governance.
