White House Strategy Signals Deregulation of Healthcare AI

The White House has released a new federal strategy on artificial intelligence that prioritizes deregulation, national infrastructure development, and international positioning. Though healthcare is not a central focus of the 28-page AI Action Plan, the implications for health systems, regulatory agencies, and AI developers in clinical contexts are substantial.

Led by President Donald Trump through an early 2025 executive order, the strategy positions AI as both an economic catalyst and a matter of national security. For health IT leaders, the consequences will not hinge on the frequency of healthcare’s mention in the plan, but rather on the structure of incentives, the treatment of oversight, and the political framing of AI as a strategic race. These factors are likely to affect everything from clinical pilot programs to funding eligibility for research and procurement.

Deregulation as a Primary Lever

The White House plan frames existing regulation as a barrier to AI adoption and asserts that agencies must dismantle outdated frameworks. To accelerate innovation, the administration directs the Office of Management and Budget and the Office of Science and Technology Policy to review and repeal rules that slow AI integration across sectors. While no healthcare-specific mandates are included, this deregulatory push could reduce federal friction for AI deployment in areas such as imaging diagnostics, administrative automation, and patient engagement tools.

The plan’s emphasis on speed and flexibility poses a direct challenge to agencies such as the Food and Drug Administration and the Centers for Medicare & Medicaid Services. These bodies have traditionally prioritized safety, efficacy, and fairness in evaluating new technologies. It is unclear how these oversight standards will reconcile with the administration’s call for a “try-first” AI culture.

A recent analysis from the Government Accountability Office identified gaps in federal coordination on AI regulation and warned that undefined jurisdictional boundaries could create systemic risks. These findings suggest that healthcare stakeholders will need to actively monitor whether new deregulatory mechanisms maintain clinical safeguards or simply remove them.

New Infrastructure and Standards

The National Institute of Standards and Technology will assume a leading role in coordinating AI evaluation frameworks. Its domain-specific work will include healthcare, with goals to establish common metrics, facilitate adoption, and measure productivity gains. This could introduce long-awaited consistency for evaluating accuracy, reproducibility, and decision support reliability in clinical models.

However, the plan also states that the NIST AI Risk Management Framework will be revised to exclude references to diversity, equity, inclusion, climate change, and misinformation. These omissions shift the standard-setting posture away from social impact considerations, which many health systems have embedded into their governance models.

Studies published by journals such as Health Affairs and the New England Journal of Medicine have underscored the importance of equity-driven auditing in preventing biased AI deployment. By narrowing the evaluative scope, the revised federal approach may not align with institutional ethics frameworks or equity mandates embedded in clinical transformation programs.

Regulatory Sandboxes and Conditional Funding

The White House proposes the creation of AI Centers of Excellence and regulatory sandboxes to facilitate pilot testing in lower-risk environments. These structures could serve as accelerators for clinical innovation if they offer formal routes to experimentation that are compatible with existing patient safety and data protection standards.

The administration also outlines a plan to restrict federal AI funding from flowing to states with what it describes as “burdensome” regulations. Though the plan does not name any states, California has enacted early-stage legislation focused on algorithmic accountability, which may fall into this category. This introduces a new dynamic for academic health systems and research centers operating in high-regulation jurisdictions.

The Kaiser Family Foundation has previously warned that uneven policy landscapes may deepen inequities in access to advanced technologies. If funding eligibility becomes contingent on regulatory alignment, the geography of innovation may shift away from established clinical research hubs and toward states with fewer constraints, regardless of healthcare system readiness.

Workforce, Procurement, and Adoption Timelines

The administration’s workforce strategy includes tax-incentivized training, a federal AI talent exchange, and guidance on upskilling for public-sector employees. These components mirror concerns voiced by hospital CIOs and population health leaders, many of whom report that their clinical teams lack sufficient training to engage with AI tools safely and effectively.

According to a 2024 survey by Fierce Healthcare, fewer than one-third of hospitals had implemented AI education into clinical workflows. By tying workforce development to reimbursement policy and procurement standards, the federal government may help accelerate workforce readiness.

The plan also proposes a unified approach to AI procurement across federal agencies. If adopted, this could serve as a benchmark for private-sector vendor evaluation. As with other sectors, health systems may choose to mirror federal contracting practices to simplify risk assessment and justify investment in emerging solutions.

A Shifting Strategic Environment

The AI Action Plan introduces a series of structural changes that reflect a broader pivot in national technology strategy. While it does not impose new mandates on the healthcare sector, it alters the policy environment in which clinical and operational decisions will be made. Health systems that have invested heavily in governance and oversight may now face new pressures to shorten validation cycles and experiment more freely.

At the same time, these shifts will demand greater internal vigilance. The absence of uniform federal standards for risk, bias, and model transparency means that healthcare organizations may need to set their own thresholds while balancing speed with institutional accountability.

For senior leaders in clinical operations, compliance, technology infrastructure, and digital innovation, this is a moment that requires strategic clarity. The policy landscape is changing rapidly. Executive decisions about partnerships, pilots, and procurement must now account for a federal stance that treats AI adoption as a matter of national urgency without clear regulatory guardrails to define success or prevent harm.