A Futures Framework for Clinical AI Governance: Anticipating Emerging Risks, Shifting Roles, and Regulatory...

Area of Science:

• Healthcare governance
• Artificial intelligence (AI) ethics
• Sociotechnical systems analysis

Background:

• Current clinical AI governance primarily focuses on near-term validation and retrospective risk detection.
• Existing frameworks are less equipped to handle the complexities of adaptive, autonomous AI systems deeply integrated into healthcare.
• The increasing sophistication and integration of AI in clinical settings necessitate forward-looking governance strategies.

Purpose of the Study:

• To develop the Futures Framework for Clinical Artificial Intelligence Governance (FF-CAIG), a conceptual and anticipatory framework.
• To organize emerging governance challenges associated with clinical AI, particularly for longer-horizon sociotechnical change.
• To provide a structured analytic approach for prospective and systems-oriented clinical AI governance.

Main Methods:

• Grounded in futures methodologies: the 3 horizons model, scenario planning, and causal layered analysis.
• Operationalized through an emerging clinical AI risk taxonomy linking futures methods to governance domains.
• Outputs include horizon classification, risk-domain mapping, scenario stress-testing, accountability-chain mapping, and horizon-scaled minimum governance actions.

Main Results:

• The FF-CAIG framework addresses near-term, transitional, and longer-term governance horizons.
• Proposes cross-horizon priorities: enhanced predeployment equity evaluation, clearer life cycle accountability, clinician AI oversight competencies, and safeguards for autonomous AI.
• Illustrates application through representative clinical AI deployment patterns, acknowledging limitations like compliance burdens and need for validation.

Conclusions:

• FF-CAIG provides a structured analytic approach for prospective clinical AI governance, not a prescriptive policy.
• Aims to support regulators, health system leaders, developers, and researchers in navigating complex AI governance challenges.
• Emphasizes the need for systems-oriented strategies to manage the evolving landscape of AI in healthcare.