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

Clinical Snapshot

Key Findings

• Effect Size: Not applicable — no empirical effect size reported; this is a conceptual framework paper

• Primary Outcome: Development of FF-CAIG, a structured anticipatory governance framework organised across three temporal horizons (near-term, transitional, longer-term) and operationalised through a clinical AI risk taxonomy linked to futures methodologies

• Nnt Or Sensitivity: Not applicable — no diagnostic, therapeutic, or prognostic outcomes measured; framework outputs are qualitative governance artefacts (horizon classifications, risk-domain maps, scenario stress-test findings, accountability-chain maps, minimum governance action sets)

• Confidence Interval: Not applicable — no quantitative analysis performed

Clinical Application

Feasibility is uncertain and likely variable. The framework requires multidisciplinary expertise spanning futures methodologies, clinical informatics, regulatory science, and health equity — a combination unlikely to be readily available in most health services. Smaller institutions and under-resourced health systems face disproportionate compliance and implementation burdens, as the authors themselves acknowledge. Practical adoption will require significant simplification, toolkits, or external facilitation. Australia's clinical AI governance landscape is evolving rapidly. The TGA regulates software as a medical device (SaMD) under the Software as a Medical Device framework, with increasing attention to AI/ML-enabled devices. The Australian Digital Health Agency and RACGP have both issued guidance on AI in primary care, though comprehensive life cycle governance frameworks remain nascent. FF-CAIG's emphasis on predeployment equity evaluation is directly relevant to Australia's obligations under the National Safety and Quality Health Service Standards and health equity commitments to Aboriginal and Torres Strait Islander communities. The framework's horizon-scaled approach could usefully inform the development of Australian-specific AI governance standards, though adaptation to the TGA regulatory pathway and PBS reimbursement context would be required. No PBS or TGA listing is relevant to this conceptual framework itself. Health system leaders, clinical governance committees, regulators, and clinical AI developers in any jurisdiction deploying or evaluating clinical AI systems. Most immediately applicable to institutions with existing AI governance infrastructure seeking to extend oversight toward longer-horizon and anticipatory risk management.

Abstract

This viewpoint develops the futures framework for clinical artificial intelligence governance (FF-CAIG), a conceptual and anticipatory framework for organizing emerging governance challenges in clinical artificial intelligence (AI). Although life cycle-oriented oversight is increasingly reflected in clinical AI regulation and institutional governance, existing approaches remain more developed for near-term validation, current-state assurance, and retrospective risk detection than for longer-horizon sociotechnical change. This gap is increasingly relevant, as AI systems become more complex, adaptive, and autonomous, and as they become more deeply embedded in care relationships and accountability structures. FF-CAIG is grounded in 3 futures methodologies: the 3 horizons model, scenario planning, and causal layered analysis. It is operationalized through an emerging clinical AI risk taxonomy that links these methods to governance domains. Its practical outputs include horizon classification, risk-domain mapping, scenario stress-testing findings, accountability-chain mapping, and horizon-scaled minimum governance actions for deployment or continued use. Applied across near-term, transitional, and longer-term horizons, the framework proposes cross-horizon priorities, including stronger predeployment equity evaluation, clearer life cycle accountability, clinician AI oversight competencies, and safeguards for increasingly autonomous or AI-mediated care systems. We illustrate FF-CAIG through 3 representative clinical AI deployment patterns and discuss its limitations, including differential compliance burdens, risks of overdocumentation, variable feasibility across jurisdictions, and the need for empirical validation. FF-CAIG is intended not as a prescriptive policy instrument or validated assessment tool, but as a structured analytic approach for regulators, health system leaders, developers, and researchers seeking prospective and systems-oriented approaches to clinical AI governance.

References

1. 1.Yang, Y., Liu, J., & Liu, S. (2026). A futures framework for clinical AI governance: Anticipating emerging risks, shifting roles, and regulatory challenges. Journal of Medical Internet Research. https://doi.org/10.2196/96152