More than 7,000 public comments later, HHS officials say healthcare organizations want coordination and governance guidance before they will trust AI tools with patient care, not faster regulatory approval.

 

What happened

The Department of Health and Human Services held a public webinar in late June 2026 to share findings from its Request for Information on accelerating the adoption of artificial intelligence in clinical care, a process HHS launched in December 2025 as part of the Make America Healthy Again (MAHA) initiative. According to the Federal Register filing, HHS received more than 7,000 public comments from healthcare providers, researchers, and industry groups before the comment period closed on February 23, 2026. HHS sought input on three areas: how digital health and software regulatory frameworks should change to account for AI tools while maintaining patient safety, whether reimbursement structures could be better aligned to support AI adoption, and how research and development investments could strengthen implementation. According to Federal News Network, HHS Deputy Chief AI Officer Arman Sharma and National Coordinator for Health IT Dr. Thomas Keane presented the webinar findings, identifying three consistent themes across the comments, healthcare organizations want better coordination across HHS agencies, support in implementation and governance structure creation, and clearer guidance on what distinguishes an effective AI tool from an ineffective one.

 

Going deeper

The webinar addressed only two of the RFI's three original focus areas, regulation and research and development, with reimbursement policy explicitly noted as out of scope for this update. HHS framed AI as a third lever in its healthcare strategy, alongside lifestyle interventions and traditional care infrastructure. Officials pointed to specific projects already underway as evidence of momentum: the Advanced Research Projects Agency for Health is developing AI agents intended to autonomously manage cardiovascular disease care, and the Administration for Community Living has launched a competition for developers to build AI tools supporting caregivers of older Americans with disabilities. The FDA is working on greater regulatory clarity covering what AI-enabled medical technologies require approval, new policy proposals for autonomous AI systems specifically, and a total product lifecycle approach that accounts for how AI tools behave differently after deployment than during pre-market testing. FDA officials indicated they expect to release policy proposals for public stakeholder comment in the near term.

 

What was said

Arman Sharma, HHS deputy chief AI officer, stated during the webinar as reported by Healthcare Dive, "We believe that starting with these three things and acting on constant engagement from this community is what's needed to establish trust. And trust in this technology is the only thing that will lead to responsible, but also effective, adoption." Dr. Thomas Keane, national coordinator for health IT, added, "Our goal is to improve access, affordability and the impact of healthcare through technology, including AI." Keane also said that regulatory approaches need to account for changing usage patterns, noting that clinicians and patients interact with AI tools differently now than they did even a few years ago.

 

In the know

Healthcare spending context frames why HHS is treating AI adoption as an economic priority rather than purely a clinical one. According to CMS data, US healthcare spending rose 7.3% to a record $5.7 trillion in 2025, and is projected to grow from 18% of GDP in 2024 to more than 20.5% by 2034. The American Hospital Association's formal response to the RFI says that reimbursement structures have not kept pace with AI tool costs, noting that physician payment has dropped 33% since 2001 when adjusted for inflation, and cautioned that updated AI payment structures should not come at the expense of other covered services.

 

The big picture

For healthcare organizations weighing AI investment decisions, the RFI feedback signals that federal guidance on governance and tool evaluation is coming, but reimbursement clarity is not part of the current phase. Organizations building AI governance structures now, before federal frameworks solidify, will be better positioned when HHS does finalize guidance than those waiting for a complete regulatory picture before acting. The trust gap HHS officials identified maps directly onto the compliance uncertainty many healthcare organizations already face with AI tools. According to Paubox's Shadow AI report, 95% of healthcare organizations report staff using AI tools without formal approval, and 75% of healthcare workers incorrectly assume Microsoft Copilot is automatically HIPAA compliant. HHS's stated goal of establishing governance guidance and criteria for what makes an AI tool trustworthy addresses exactly the gap that has allowed shadow AI use to proliferate largely unchecked across the sector.

 

FAQs

What is a Request for Information and how does it differ from a proposed regulation?

An RFI is a formal mechanism federal agencies use to gather public input before deciding whether and how to develop policy, without proposing specific binding rules. Unlike a Notice of Proposed Rulemaking, an RFI does not commit the agency to any particular regulatory outcome. It signals that HHS is in an information-gathering phase on AI in clinical care, not yet at the stage of drafting enforceable requirements.

 

Why did HHS separate the reimbursement policy out of this particular update?

The RFI covered three levers: regulation, reimbursement, and research and development. HHS officials explicitly stated that reimbursement was out of scope for the June webinar, focusing instead on regulatory clarity and R&D investment. This suggests reimbursement policy, which involves negotiations with CMS payment structures, is following a slower or separate development track than the regulatory guidance work.

 

What does "total-product-lifecycle" regulation mean for AI medical devices?

Traditional medical device regulation focuses heavily on pre-market approval, testing a device before it reaches the market. A total-product-lifecycle approach extends oversight into how the device performs after deployment, which matters specifically for AI tools because their behavior can change as they process more real-world data or as underlying models are updated, a phenomenon sometimes called model drift.

 

How does the AHA's reimbursement critique affect health systems considering AI adoption?

The AHA's comments show that current payment structures may not adequately cover the costs hospitals incur when implementing AI tools, creating a financial disincentive even when clinical benefits are clear. Health systems assessing AI investments should weigh this reimbursement uncertainty against projected efficiency gains, since federal payment policy has not yet caught up to the technology.

 

What HHS-backed AI projects are already underway that provide a preview of upcoming initiatives?

The Advanced Research Projects Agency for Health is developing AI agents aimed at autonomously managing cardiovascular disease care, and the Administration for Community Living has launched a developer competition for AI tools supporting caregivers of older adults with disabilities. Both projects offer an early look at the kind of clinical and administrative AI applications HHS intends to support as its broader AI strategy develops.