The U.S. Department of Health and Human Services (HHS) has issued a request for information (RFI) titled “Accelerating the Adoption and Use of Artificial Intelligence as Part of Clinical Care.” The RFI invites stakeholders — such as health systems, providers, payers, patients, life sciences companies, and health technology developers — to provide broad public input on how HHS can leverage AI to improve clinical care delivery, enhance patient experiences, reduce provider burden, and help lower healthcare costs.
HHS is seeking feedback on how it may best utilize its regulatory, reimbursement, and research and development authorities to support the safe, effective, and equitable integration of AI into clinical settings. The deadline to submit comments is February 23, 2026. As of February 19, 2026, only 70 comments have been submitted. By submitting comments, parties have a unique opportunity to potentially shape emerging federal AI policy.
The RFI highlights key themes that include interoperability, data privacy and security, patient safety, and trust. These underscore not only a continued focus on responsible AI adoption in healthcare but highlight how regulators are grappling with the novel and rapidly evolving landscape as AI becomes more prolific in society.
Responses may inform future policy development, payment structures, and agency priorities relating to digital health and AI oversight. One area stakeholders may wish to address is whether the federal government could introduce financial incentives tied to AI adoption. One example is augmented reimbursement for clinicians that successfully implement AI-enabled tools versus penalties, in the form of reduced payments, for those that do not.
Such policy approaches could significantly affect provider operations and economics. Smaller and independent physician practices, in particular, may argue that the costs of adopting AI technologies and complying with related regulatory requirements outweigh any available incentives. By contrast, providers in larger, well‑integrated health systems that already incorporate AI‑enabled models into day‑to‑day operations are better positioned to benefit from favorable reimbursement policies without incurring comparable administrative burdens.
Commenters may wish to highlight practical challenges, including technology acquisition costs, workflow integration, training, cybersecurity, data governance, and liability considerations.