Table 2.

Governance domains, gaps, and proposed actions for addressing AI-related inequalities in health systems

Domain	Current gaps	Proposed action	Cost of inaction
Legal frameworks	Fragmented governance; restrictive licensing; limited LMIC data-sharing	Align with FAIR principles; embed equity in EHDS-type frameworks; benefit-sharing agreements	Data poverty persists; AI underperforms for underserved populations
Evidence generation	Poor demographic reporting; N-of-1 LLM evaluations; no equity endpoints	Mandate subgroup reporting; repeated-sampling designs; a priori acceptability thresholds	Technologies approved on evidence blind to at-risk populations
Regulation & market access	No AI-specific representativeness standards; volume-based payment	Minimum representativeness for market access; equity incentives; value-based payment	Benefits concentrated in commercially attractive populations
Workforce	Inconsistent AI curricula; unequal training access; deskilling risk	Systematic competency integration; equitable resourcing; atrophy safeguards	Two-tier care quality by provider training access
Public trust	Limited citizen voice; transparency deficits; environmental concerns	Democratic oversight; environmental impact in evaluation; community engagement	AI lacks social licence; reforms lack legitimacy