- The Department of Health and Human Services laid out three priorities to foster the adoption of artificial intelligence in clinical care
- The priorities are based on feedback from a request for information that the agency issued in December
- The three priorities center on clearer implementation standards, better government guidance and improved coordination with industry
The Department of Health and Human Services announced Thursday that it will focus on creating an implementation guidance and clearer standards for the adoption of artificial intelligence in clinical care, according to a GovCIO report.
The agency declared three priorities based on the results of its request for information released in December, which received over 7,000 responses. The priorities also included better coordination between agencies such as the Advanced Research Projects Agency for Health.
HHS, the Defense Health Agency and related federal departments will discuss their contracting priorities for fiscal year 2027 at the Potomac Officers Club’s 2026 Healthcare Summit on December 3. Don’t miss the opportunity to expand your network and initiate business prospects. Register early.
What Was the Focus of the RFI?
HHS sought industry input on the biggest barriers to the use of AI in clinical care as well as the innovation challenges preventing its adoption. The agency also asked if there were any regulatory or programming changes it should make to facilitate clinical AI use. In addition, the RFI asked respondents what novel AI tool would be best in improving clinical care.
What Did HHS Deputy AI Chief Arman Sharma Say About the RFI Results?
HHS Deputy Chief AI Officer Arman Sharma said the three priorities is the foundation of establishing trust between the clinical care community and the government. Sharma highlighted the Agentic AI-Enabled Cardiovascular Care Transformation program, or ADVOCATE, as the main project of the Advanced Research Projects Agency for Health in terms of expediting AI adoption. However, he acknowledged the need for government frameworks and guidance to support AI implementation in clinics.
“We recognize that even with the most agile regulation, even with optimal reimbursement, there’s still an implementation gap,” he explained. “There’s still process and workflow optimization that’s required to take AI and bring it into existing workflows within any care setting, whether that be a small clinic or whether that be a large academic medical center.”
