A conceptual framework for measuring AI health equity

Clinical Snapshot

Key Findings

• P Value: Not applicable — conceptual/viewpoint paper

• Effect Size: Not applicable — no empirical data presented

• Primary Outcome: Proposal of the AI in Healthcare Equity Index (AIHEI), a composite framework assessing health AI equity across five domains: data representation, algorithmic fairness, transparency and explainability, governance and oversight, and community impact and benefit sharing

• Nnt Or Sensitivity: Not applicable — no clinical outcome data; framework is at pre-validation conceptual stage

• Confidence Interval: Not applicable — no quantitative analysis conducted

Clinical Application

Currently low for direct clinical application. The AIHEI requires empirical piloting, domain weighting specification, and psychometric validation before it can be used as a reliable procurement or regulatory tool. Feasibility will vary substantially by health system capacity, data infrastructure, and regulatory maturity. The AIHEI framework has potential relevance to Australian health AI governance, particularly given the TGA's evolving Software as a Medical Device (SaMD) regulatory framework and the Australian Digital Health Agency's AI strategy. The framework's emphasis on data representation is pertinent to known disparities in AI performance for Aboriginal and Torres Strait Islander populations, where training datasets are frequently non-representative. RACGP guidance on digital health tools and the National Health and Medical Research Council (NHMRC) Statement on Responsible AI in Health could provide natural homes for an adapted AIHEI. PBS and MBS implications are indirect at this stage but would become relevant if AIHEI scores were incorporated into health technology assessment processes. Australian adoption would require contextual adaptation, particularly around Indigenous data sovereignty principles consistent with the AIATSIS Code of Ethics. Health system administrators, AI developers, regulators, procurement bodies, and clinicians involved in evaluating, deploying, or overseeing AI-driven health technologies — particularly in settings serving diverse or underserved populations

Abstract

BACKGROUND: Artificial intelligence (AI) is increasingly embedded in health systems globally and has the potential to improve efficiency, diagnostic accuracy, and decision support. However, its benefits remain unevenly distributed, particularly in low- and middle-income countries (LMICs). Models developed using datasets from specific populations may perform poorly in other settings, reinforcing structural inequities rather than correcting them. OBJECTIVE: This viewpoint proposes a composite framework, the AI in Healthcare Equity Index (AIHEI), to support measurable assessment of equity in health AI systems. FRAMEWORK: The AIHEI is designed to assess equity across five domains: data representation, algorithmic fairness, transparency and explainability, governance and oversight, and community impact and benefit sharing. By generating a standardised score, the index could enable comparisons across technologies, incentivise improvement, and support regulation, procurement, publication, and funding decisions. IMPLICATIONS: Pilots across diverse health domains and geographic settings are needed to assess feasibility, refine domain weighting, and evaluate reliability, reproducibility, and validity. Important challenges include contextual definitions of fairness, data sovereignty, post-deployment monitoring, and the risk of metric gaming. CONCLUSIONS: Quantifying equity in health AI is essential to ensure that AI does not create, widen, or exacerbate existing disparities by neglecting underserved populations. A common, objective measure of AI-related health equity can help move the field from ethical aspiration toward measurable accountability, monitoring, and enforcement.

References

1. 1.Njei, B., Kanmounye, U. S., Bain, L. E., Al-Ajlouni, Y. A., Ogedegbe, O., Sobhia, M. E., Patel, R. C., Anand, S. S., & Tita, A. (2026). A conceptual framework for measuring AI health equity. International Journal for Equity in Health. https://doi.org/10.1038/s41467-024-46503-5 [Note: DOI string corresponds to Nature Communications — bibliographic verification recommended prior to citation]