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With the recent unveiling of America’s AI Action Plan, the U.S. government is putting AI innovation at the center of its economic, defense, and technological strategy. While the plan is geared toward government execution, its ripple effects for the private sector—especially digital health companies—are undeniable.
If you’re building AI-driven health tech (honestly, who isn’t?), now’s the time to pay attention. The current administration has announced its intention to position you for success as part of its plan to “win” the global AI race.
The AI Action Plan, in a Nutshell
This isn't your average policy document. The Plan is a sweeping vision for AI dominance with three key pillars:
- Accelerating AI Innovation – Cutting red tape and supporting open-source development.
- Building AI Infrastructure – From semiconductors to secure data centers.
- Leading International AI Diplomacy – Export controls, alliances, and national security priorities.
While it reads like a government playbook, much of the execution depends on—and directly impacts—private industry.
Becker’s Healthcare has done another lovely summary, which you can read here. In this post, my focus will be on takeaways for health tech companies in particular.
Why Health Tech Companies Should Care
1. AI is a National Priority—Including in Healthcare
The plan calls out healthcare as a prime candidate for AI acceleration. That includes funding AI test zones and establishing regulatory “sandboxes” to make it easier for companies to test innovations without the traditional FDA slowdown.
2. Health AI is Strategic AI
When you’re building models that diagnose disease, personalize care, or optimize hospital operations, you're not just innovating—you’re contributing to what the plan calls an “industrial and information revolution.”
Spotlight: AI Sandboxes—A Workaround for FDA Overhead?
One of the most compelling elements in America’s AI Action Plan—especially for digital health innovators—is the proposed creation of AI regulatory sandboxes. These aren't just theoretical zones. They’re envisioned as real-world testbeds where startups and researchers can deploy AI solutions in highly regulated industries like healthcare—without immediately triggering full-blown regulatory enforcement.
Think of it like a clinical trial site for algorithms. Sandboxes would allow companies to:
- Pilot new AI technologies (like diagnostic or therapeutic tools) in collaboration with federal agencies.
- Test real-world use cases in hospitals, clinics, or public health systems.
- Collect performance data while bypassing standard FDA scrutiny—at least temporarily.
These would be coordinated by key regulatory agencies like the FDA and NIST, with oversight but not full application of current regulations.
Why It Matters for AI-SaMD Innovators
If you're building AI Software as a Medical Device (AI-SaMD)—like decision-support tools, imaging analysis models, or patient triage bots—you know the FDA’s regulatory net can be both a cost and time killer.
The Action Plan’s sandbox model could offer:
- Regulatory breathing room to gather evidence and refine your tool before formal submission.
- Early collaboration with evaluators to align your system with evolving expectations.
- More iterative innovation, enabling faster pivots and updates, especially for adaptive or learning systems.
There’s a catch: these sandboxes aren’t regulatory loopholes. They’re regulated zones of experimentation. Expect to share data, engage in evaluations, and accept public transparency. But if you’re navigating the blurry line between wellness tools and regulated SaMDs, this could be your green light to move faster and smarter.
Spotlight: AI Evaluations—The New Benchmark for Trust and Compliance
If you’re building AI in a regulated field like healthcare, performance alone is no longer enough to close deals. You’ll need to prove it—repeatedly, reliably, and transparently. My clients selling to large institutional healthcare buyers like health systems and payors, are facing increasingly more sophisticated and robust AI Governance processes, all of which differ by customer. That challenge is at the heart of the Action Plan’s plan for building an AI evaluation ecosystem.
What Are AI Evaluations?
In short: they’re structured, repeatable tests to assess an AI system’s accuracy, reliability, robustness, and safety. Think of them as the clinical trials of the algorithm world. These evaluations can demonstrate:
- How consistent your AI behaves in edge cases.
- Whether your system is biased, overconfident, or unsafe.
- How well it complies with existing legal frameworks.
For health AI, these evaluations are especially critical. A diagnostic model that’s 95% accurate in training data might behave very differently in the wild—across demographics, data quality, or care settings.
What the Government's Planning
The Action Plan proposes a comprehensive framework for AI evaluations, including:
- Federal evaluation guidelines from NIST and its Center for AI Standards and Innovation (CAISI).
- Sector-specific testbeds—including like healthcare—where AI systems can be trialed in secure, real-world environments.
- Biannual convenings to share insights across agencies and academia, helping set emerging benchmarks and best practices.
- A government-backed NIST AI Consortium, charged with establishing scalable and interoperable evaluation metrics.
Why This Matters for Digital Health
If you're pitching to hospitals, payers, or the federal government, you'll need to show more than internal test results. Evaluations will likely become:
- A de facto requirement for regulatory consideration.
- A competitive differentiator in procurement and public trust.
- A tool for transparency, especially if your product evolves post-deployment (e.g., with continual learning).
For those familiar with this concept, you’ll likely notice the close alignment between this priority and the vision of the Coalition for Health AI (CHAI): building a nationwide network of Assurance Resource Providers (ARPs), advancing testing and evaluation standards, prioritizing secure and federated access to health data.
Other Opportunities for Digital Health Companies
Open-Source Model Incentives
Open-source and open-weight models get strong support in the plan—good news for companies that want transparency and control. Further, because some organizations (commercial and academic) may not trust closed models to ingest their healthcare or other sensitive personally identifiable data, access to more open models could be a massive unlock for companies whose use case involves ingestion and interpretation of customer data.
Access to Government Data and Compute
Through the National AI Research Resource (NAIRR), companies may gain access to powerful compute resources and new scientific datasets—think genomics, biosurveillance, and materials science. Specifically, the plan directors the creation of controlled access to “AI-ready” restricted Federal data. Today, this data is extremely difficult to access in a way that benefits the private sector (e.g., CMS’s Qualified Entity program).
What to Watch For
Regulatory and Compliance Risks
There’s a strong anti-regulation stance in the Plan (even though it also calls for more regulations to support it). While that may mean fewer compliance headaches, it could also lead to uncertainty in ethical and legal expectations—it will be important to follow relevant rulemaking.
Political Pressure and “American Values”
Digital health tools may be scrutinized for ideological “neutrality.” If your AI model addresses controversial issues (mental health, reproductive care, gender), you’ll need a plan for navigating government expectations.
Location-Based Funding Bias
The Plan suggests that AI funding could bypass states with restrictive AI laws. Where you’re headquartered might affect your eligibility for grants or partnerships.
The Bottom Line
The AI Action Plan is more than a federal roadmap—it’s a national rallying cry. For digital health companies, it’s a call to align with larger strategic goals. Those who do could gain access to funding, infrastructure, data, and influence.
And if you’re building AI-SaMD products? This may be your chance to innovate without the regulatory handcuffs—at least long enough to prove your value.
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