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Related Concept Videos

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Issues And Trends In Healthcare Delivery System

The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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A Futures Framework for Clinical AI Governance: Anticipating Emerging Risks, Shifting Roles, and Regulatory

Yi Yang1, Jialin Liu1,2,3, Siru Liu4

  • 1Information Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China.

Journal of Medical Internet Research
|June 29, 2026
PubMed
Summary
This summary is machine-generated.

This viewpoint introduces the Futures Framework for Clinical Artificial Intelligence Governance (FF-CAIG) to address long-term challenges in AI oversight. FF-CAIG offers a structured approach for managing complex, adaptive AI systems in healthcare settings.

Keywords:
artificial intelligenceartificial intelligence governanceclinical AIclinical AI governanceclinical workforcedigital health policyemerging risksfutures studieshealth regulationstrategic foresight

Related Experiment Videos

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.