Strategic Governance Tools for AI in Digital Health: A Scoping Review Across the Innovation Lifecycle and ESG Dimensions

Abstract

Purpose: As artificial intelligence (AI) systems become integral to healthcare value chains, business and policy leaders face pressure to implement governance mechanisms that ensure technical robustness, ethical accountability, and environmental sustainability. This study aims to map and evaluate existing tools, frameworks, and guidelines for responsible AI governance in digital health.Study design/methodology/approach: A scoping review was conducted following the PRISMA-ScR framework, covering literature published between 2015 and 2025 across academic and grey sources.Sample and data: From 105 records screened, 46 documents met inclusion criteria. These tools were assessed across the AI innovation lifecycle (design, development, deployment, governance, and assessment), their intended stakeholder groups (e.g., developers, policymakers, end users), and their alignment with ESG principles such as transparency, accountability, sustainability, and inclusivity.Results: Most frameworks originated in Europe and North America and concentrated on early-stage processes, particularly design (59%) and governance (72%). Developers and policymakers were the primary audiences. Transparency (76%) and accountability (70%) were emphasized, while environmental sustainability (17%) and user-centered inclusivity received limited attention.Originality/value: This review provides the first systematic synthesis of AI governance tools in healthcare, explicitly through an ESG lens, highlighting both strengths and blind spots in current practices.Research limitations/implications: While the study captures global literature, the dominance of Western sources may limit the representativeness of findings. Findings underscore the need for scalable, lifecycle-spanning, and context-sensitive governance mechanisms that support sustainable and equitable adoption of AI in health systems.

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