Navigating AI Governance in Health Systems: A Roadmap for Executives
- Dr. Gillie Gabay
- 7 days ago
- 4 min read
Introduction
Artificial Intelligence (AI) is reshaping healthcare delivery and management. Algorithms can assist us with imaging diagnostics, predict patient deterioration, streamline revenue cycles, and even inform decisions about care coordination. Yet, while health systems accelerate the adoption of AI, they may lack a coherent governance framework to oversee the AI tools and their implementation in practice, exposing themselves to risks of clinical harm, reputation damage and costly lawsuits. Therefore, responsible AI governance is a strategic imperative that challenges executives and policymakers. In this article I propose a roadmap for healthcare executives to govern AI responsibly proactively ensuring an alignment with safety, equity, ethics, and long-term value.
AI adoption in health systems is growing exponentially. While operational systems such as AI for billing or scheduling can result in inefficiencies, AI clinical tools can support decisions that directly affect patient care such as triage prioritization, alerts of pressure injuries, or radiology interpretations, in which errors have real and lasting consequences on human life and provider wellbeing. According to a 2024 Health Affairs report, over 60% of large U.S. hospitals have deployed AI tools in at least one clinical or operational function. However, less than a third have dedicated policies or oversight committees specific to AI, creating a risky chasm. Without clear governance, health systems face challenges in clinician trust and legal liability. The pressure is growing as regulatory bodies like the FDA and the Office of the National Coordinator for Health Information Technology are moving toward stricter oversight of AI tools, especially those embedded in electronic health records and clinical workflows. Figure 1 presents the life cycle of AI tools that executives must consider and follow for effective governance.
Design & Development
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Validation & Risk Testing
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Clinical Integration
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Real-World Monitoring
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Feedback & Model Updating
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Scaling |
Figure 1: AI Tool Lifecycle in Healthcare
Responsible AI governance is not simply about compliance, it’s about trust, strategy, and stewardship. It ensures that innovation aligns with organizational values and public interest. To allocate resources efficiently and avoid overregulation, executives should classify AI tools by impact level, from low risk (e.g., appointment reminders, chatboxes), to moderate risk (staffing models), through high risk (diagnosis or treatment recommendations). Each tier should have escalating levels of review, validation, and monitoring. Establishing this taxonomy will ensure that high-risk applications receive appropriate scrutiny. Figure 2 presents the proposed AI Governance Framework for Health Systems
AI tools must be rigorously validated before deployment, especially those used in patient-facing decisions. Validation should cover the accuracy and generalizability, the performance across diverse patient populations, equity and bias testing. Executives must ensure that clinical leaders are involved early in model evaluation. Clinicians can assess whether the tool aligns with workflows, supports judgment (rather than replacing it), and avoids cognitive overload. For example, a health system that deploys a stroke detection algorithm should validate its accuracy across different age groups and ethnic populations before using it in emergency protocols.
Also, since trust depends on transparency, models that are difficult to assess the risk in using AI will alienate providers and reduce accountability. Health systems should favor “explainable AI” whenever possible tools that provide not only a recommendation but also the reasoning behind it. Executives should require suppliers to disclose training data sources, representativeness, model logic, assumptions, risks, and known limitations. A formal AI governance body should oversee selection, implementation, and monitoring of AI tools to ensure a well-rounded oversight reflecting ethical, operational, and community concerns. AI is not a “set it and forget it” technology. Models degrade over time due to changes in patient populations, workflows, or external trends, a phenomenon known as model drift.
Health systems should implement continuous monitoring protocols to track performance (e.g., accuracy, false positives), disparate impact across demographics, changes in clinician behavior or override rates, post-deployment audits as key to understanding how AI is actually being used versus how it was designed. Feedback loops from users should inform retraining or removal decisions. Further, executives must create clear lines of accountability for AI governance. Who is responsible if a model causes harm? Who owns the data? Who approves updates? Clarity is critical, particularly in multi-stakeholder environments. Equally important is investing in workforce training. Providers and administrators must understand the capabilities and limitations of AI tools. AI literacy should be built into onboarding and leadership development programs.
Finally, fostering a culture of thoughtful adoption is essential. Encourage staff to raise concerns and question tools. Celebrate wins but also normalize sunsetting tools that don’t deliver value or introduce risk. Government and industry regulators also have a role to play. Policymakers can strengthen AI governance by: mandating transparency requirements for AI-based medical devices, funding research on ethical and equitable AI in healthcare, creating model procurement standards for AI tools, and providing incentives for health systems to adopt responsible AI frameworks. The FDA’s proposed framework for “Good Machine Learning Practice” is a step forward—but more support is needed for real-world implementation. Figure 3 presents the AI Risk-Tier Taxonomy in Health Systems.
To sum up, AI is a new layer of decision-making in healthcare. Without governance, it becomes a liability. With it, AI becomes a force multiplier for quality, equity, and innovation. For executives, the mandate is clear: lead the governance conversation, invest in multidisciplinary oversight, and embed ethical guardrails into every stage of the AI lifecycle. Health systems that succeed in doing so won’t just deploy AI tools, they will earn the trust required to truly transform care and achieve value-based healthcare.
Additional Reading:
Liao F, Adelaine S, Afshar M, Patterson BW. Governance of Clinical AI applications to facilitate safe and equitable deployment in a large health system: Key elements and early successes. Frontiers in digital health. 2022 Aug 24;4:931439.
Reddy S, Allan S, Coghlan S, Cooper P. A governance model for the application of AI in health care. Journal of the American Medical Informatics Association. 2020 Mar;27(3):491-7.
Wagner JK, Doerr M, Schmit CD. AI Governance: A Challenge for Public Health. JMIR Public Health and Surveillance. 2024 Sep 30;10(1):e58358.
You Z, Wang Y, Xiao Y. Analysing the Suitability of Artificial Intelligence in Healthcare and the Role of AI Governance. Health Care Analysis. 2025 Mar 6:1-33.
