A conceptual framework for measuring AI health equity

The economic imperative for an AI Productivity Index in healthcare: Beyond clinical promise to fiscal accountability

An AI Productivity Index is proposed to complement existing safety, efficacy and economic assessments by evaluating operational impact, implementation burden, opportunity costs and post-deployment consequences.

Artificial intelligence in health care and social inequality: will AI reduce or widen health gaps?

Artificial intelligence (AI) could transform health care, particularly in lowresource settings (1). The technology enables new capabilities, ranging from data acquisition to decision support, that amplify ongoing investments in digital technology to improve access to information, diagnostics, treatment, and decision-making (2). As health gaps persist between and within countries, a key question emerges: will deployable AI solutions widen or narrow existing disparities? (3). In many countries, poverty, education, geography, and race constitute risk factors that contribute both to health status and to access to health services and other determinants of health (4). Two competing hypotheses exist (5). The optimistic view holds that AI can reduce these gaps by improving the accuracy of diagnostics and therapeutics and by expanding access to services that would otherwise be out of reach demonstrates that these capabilities can profitably influence health status and survival in under-resourced settings where access to trained human capital, diagnostic devices, and therapeutics is limited (6). The gloomy view maintains that significant mechanisms exacerbate the very gaps that AI might alleviate: 1) the data on which AI training relies often reflects a chronic lack of representation, 2) the post-deployment conditions under which models operate may drift further from those on which models were trained, 3) the infrastructure and knowledge necessary to deploy new capabilities may be absent in the most vulnerable settings, and 4) oversights in governance and use could further endanger already vulnerable groups as the market for AI grows (7, 8).

Sustainable artificial intelligence in global health: balancing promise, equity, and environmental realities in low- and middle-income countries.

OBJECTIVE Artificial intelligence (AI) adoption in global health informatics is accelerating, yet scaling, sustainability, equity, and environmental challenges limit impact, particularly in Low and Middle Income Countries (LMICs). MATERIALS AND METHODS Drawing on experience from members of the American Medical Informatics Association Global Health Informatics and Climate, Health and Informatics Working Groups, we synthesized implementation, evaluation, sustainability, and governance considerations for AI in resource constrained health systems. RESULTS We propose a framework integrating four components: Green AI necessity assessments; a One Digital Health systems lens; pragmatic, workflow integrated evaluation; and federated governance supporting locally led stewardship and cross institutional learning. DISCUSSION Sustainable AI requires moving beyond short term pilots to address infrastructure, environmental costs, workflow integration, equity, and locally relevant evidence. CONCLUSION Funders and implementers should prioritize durable infrastructure, strengthened health information systems, rigorous evidence generation, environmentally responsible deployment, and local stewardship to achieve scalable, equitable, and sustainable AI in global health.

Health artificial intelligence is here, but are we measuring what matters?

The AI-Driven Healthcare Value Framework—Rethinking Traditional Care Models in the Age of Automation

Highlights What are the main findings? This study introduces a comprehensive value framework tailored to artificial intelligence in healthcare, extending evaluation beyond narrow cost–outcome ratios. The framework highlights patient, provider, organizational, and system-level impacts as essential dimensions of healthcare value. It shows that conventional healthcare value models often fail to capture equity, trust, explainability, and environmental sustainability when AI and other digital tools are used at scale. The proposed model organizes these considerations into a structured framework that can be applied to AI-enabled healthcare settings. What are the implications of the main findings? The framework can help policymakers and health system leaders judge whether AI investments truly improve population health and reduce inequities. It provides researchers with clear dimensions for developing indicators and evaluation tools for responsible AI use in health services. It supports more people-centered, ethically grounded, and sustainable decision-making in healthcare innovation. It may also guide future implementation and assessment of AI systems in ways that balance effectiveness, fairness, and long-term system value. Abstract Background: The rapid adoption of artificial intelligence (AI) in healthcare and management information systems has posed both opportunities and challenges in assessing value across clinical, operational, governance and societal dimensions. Existing healthcare value models are inadequate for the dynamic, data-rich, and ethically complex nature of AI-enabled care and thus necessitate a broader evaluative framework. Objective: This paper proposes an AI-Augmented Healthcare Value Framework (AI-HVF) designed as a multidimensional evaluative lens for assessing value in AI-enabled healthcare across structural, process, outcome, cost, and governance domains. Methods: A narrative, theory-driven literature review was undertaken of healthcare quality frameworks, value-based healthcare, digital health, AI in medicine and public health to identify limitations of existing models and to derive the required dimensions for an updated framework. The dimensions were synthesized into an integrated conceptual model linking structures, processes, outcomes, costs and governance in AI-enabled healthcare. Results: AI-HVF transforms traditional healthcare value models in a data-rich and automated care era. Structural dimensions include digital infrastructure, workforce readiness, learning health systems, and operational efficiency; process dimensions include AI-supported clinical excellence, patient experience, prevention, and explainability; and outcome dimensions go beyond traditional clinical metrics to include equity, safety, continuity, provider well-being, sustainability, and societal value. It also includes real cost accounting and has governance, ethics and trust as an overarching layer that supports accountability, transparency and fairness across all domains. Conclusions: AI-HVF provides a multi-dimensional framework to assess, plan and govern AI in health care at the patient, organization, and system levels. It is intended to enable retrospective evaluation and prospective implementation. The framework offers a foundation for developing future indicators, pilot testing and for comparative evaluation of ethical, equitable and sustainable AI integration in healthcare.

Responsible and innovative AI for mental health care: five priority themes

Five priorities define a translational agenda for 2026 and beyond: AI in mental health will succeed not through model performance alone, but through disciplined integration into clinical workflows, measurement systems, and governance structures that ensure safety, equity, and real-world effectiveness.