The White House has released the National Policy Framework for Artificial Intelligence, laying out legislative recommendations across six areas that will shape how AI is developed, deployed, and regulated across American industries. This framework comes after the release of the America's AI Action Plan in July 2025, which sets the broader strategic context, warning that "whoever has the largest AI ecosystem will set global AI standards and reap broad economic and military benefits." With regards to healthcare organizations, this framework carries certain implications.

 

A framework built for industry

The White House is explicitly steering away from heavy-handed federal regulation. The framework calls on Congress to "not create any new federal rulemaking body to regulate AI, and should instead support development and deployment of sector-specific AI applications through existing regulatory bodies with subject matter expertise." For healthcare, that means the FDA, CMS, and HHS remain the primary regulatory bodies not a new AI-specific agency. Organizations that have already invested in building regulatory relationships with these bodies are in a good position. As the Action Plan states, "AI is far too important to smother in bureaucracy at this early stage, whether at the state or Federal level."

 

1. Protecting children and what it means for patient-facing AI

The first pillar focuses on protecting minors from AI-related harms, including exploitation and self-harm. This means healthcare organizations running digital health platforms, mental health apps, or any patient-facing AI tool should pay close attention. The framework calls for AI platforms likely to be accessed by minors to "implement features that reduce the risks of sexual exploitation and self-harm to minors," and affirms that "existing child privacy protections apply to AI systems, including limits on data collection for model training and targeted advertising."

For pediatric health systems, behavioral health platforms, and any organization whose digital tools may be accessed by patients under 18, this is a direct compliance.

Read also: Risk of ungoverned AI use in healthcare

 

2. Strengthening communities

The second pillar addresses AI infrastructure buildout and community impact. Two provisions stand out for healthcare. First, the framework calls on Congress to "streamline federal permitting for AI infrastructure construction," which could increase the buildout of data centers that health systems depend on for cloud-based AI workloads. Second, it calls for augmenting "existing law enforcement efforts to combat AI-enabled impersonation scams and fraud that target vulnerable populations such as seniors."

Healthcare organizations are already experiencing AI-driven fraud. The FBI has issued resources warning that AI-generated deepfakes are being used to impersonate trusted individuals, including public figures, colleagues, and family members. The threat is the same for healthcare. As John Riggi, the American Hospital Association's national advisor for cybersecurity and risk, has warned, "Criminals are increasingly using AI-generated deep fake audio and video content — often in combination — to deceive health care staff. Deep fakes are used to manipulate unwitting individuals by having them click on phishing emails, provide their credentials, hire malicious remote IT workers or transfer funds to criminal accounts."

The FBI has also issued guidance on how criminals use AI-generated text, images, audio, and video for fraud schemes, including tips to help organizations protect themselves. A federal legislative act, as called for in this framework, gives healthcare compliance and security teams both additional legal backing and, potentially, new enforcement resources to work with. In the meantime, Riggi's advice is, "Constant vigilance and multi-layered human verification processes are needed, especially as AI-synthetic video and audio capabilities continue to advance."

 

3. Intellectual property

The framework takes a non-position on whether training models on copyrighted material constitutes fair use. The document states that "the Administration believes that training of AI models on copyrighted material does not violate copyright laws," but acknowledges that "arguments to the contrary exist" and leaves the matter to the courts.

The framework does encourage Congress to "consider enabling licensing frameworks or collective rights systems for rights holders to collectively negotiate compensation from AI providers." Health systems that own large proprietary datasets may find themselves in a stronger negotiating position as licensing frameworks emerge.

Read also: Can de-identified data be used to train AI under HIPAA?

 

4. Free speech and censorship

The directive from Congress is to "prevent the United States government from coercing technology providers, including AI providers, to ban, compel, or alter content based on partisan or ideological agendas." The Action Plan is direct on this point, stating that AI systems "must be free from ideological bias and be designed to pursue objective truth rather than social engineering agendas when users seek factual information or analysis."

Healthcare AI tools that surface clinical recommendations, treatment guidelines, or public health information are susceptible to political pressure about what content they amplify or suppress. This provision, if enacted, would constrain the federal government's ability to pressure AI vendors into shaping clinical content outputs for non-clinical reasons.

Related: Real-world examples of healthcare AI bias

 

5. Enabling innovation

The Action Plan calls for establishing "regulatory sandboxes or AI Centers of Excellence around the country where researchers, startups, and established enterprises can rapidly deploy and test AI tools while committing to open sharing of data and results," with the FDA named as an enabling agency. For healthcare, regulatory sandboxes could offer a structured pathway to pilot AI diagnostics, autonomous clinical workflows, and AI-assisted drug discovery without being immediately subject to FDA clearance or CMS coverage determination processes.

The Action Plan acknowledges that "many of America's most critical sectors, such as healthcare, are especially slow to adopt due to a variety of factors, including distrust or lack of understanding of the technology, a complex regulatory landscape, and a lack of clear governance and risk mitigation standards." Additionally, the call to "provide resources to make federal datasets accessible to industry and academia in AI-ready formats for use in training AI models" is directly relevant to health AI. Federal datasets like Medicare claims data, NIH research repositories, and CDC surveillance data, if made more accessible in AI-ready formats, could accelerate model development for population health management, predictive analytics, and care gap identification.

Read also: How AI promises a healthier future

 

6. Workforce development

The sixth pillar addresses AI readiness in the American workforce. The Action Plan states that "AI will improve the lives of Americans by complementing their work — not replacing it," and calls for prioritizing "AI skill development as a core objective of relevant education and workforce funding streams." Organizations that formalize AI training pathways now, and engage with the land-grant and community college programs highlighted in this framework, will build a more future-ready clinical and administrative workforce.

Learn more: How healthcare organizations should train staff on AI use

 

7. Federal preemption

The Action Plan is direct on this point, recommending that the federal government "not allow AI-related Federal funding to be directed toward states with burdensome AI regulations." For organizations operating across multiple states, navigating multiple AI regulatory regimes is an operational and compliance burden. A federal law, if enacted, would simplify compliance.

 

What healthcare organizations should do now

This framework is legislative guidance, not law. However, the Action Plan signals that the Administration is already acting. Healthcare organizations should engage their government affairs teams, revisiting AI governance policies, auditing patient-facing AI tools for minor-access risks, and positioning themselves to participate in regulatory sandbox opportunities as they emerge. The organizations that engage proactively with this framework will be far better prepared for the regulations that follow.

 

FAQs

Does this framework apply to all healthcare organizations or just large health systems?

The framework applies across the healthcare sector, including physician practices, digital health startups, payers, and med-tech companies of all sizes.

 

How does this framework interact with HIPAA?

The framework does not replace or modify HIPAA, which remains the main privacy and security standard governing AI systems that handle protected health information.

 

What should smaller healthcare organizations with limited compliance resources prioritize?

Smaller organizations should focus first on auditing patient-facing AI tools, reviewing data governance policies, and engaging with any regulatory sandbox opportunities that emerge in their region.