Dec. 19, 2024

The bipartisan U.S. House Task Force on Artificial Intelligence (AI) released its highly anticipated report on AI Dec. 17. The nonbinding report is intended to inform policymaking activities and priorities of the next Congress and covers a broad range of cross-cutting and sector-specific issues, including those related to the healthcare sector. The American College of Radiology® (ACR®) met with congressional offices and provided comments in May to inform the task force’s efforts.

In general, the task force supports leveraging and expanding the authorities of existing regulatory agencies within the specific sectors instead of creating a centralized AI agency. With regard to healthcare, the task force recommends:

  • Ensuring AI safety/transparency, particularly for AI that denies or approves care/coverage.
  • Supporting the National Institutes of Health and other agencies’ research into healthcare AI.
  • Creating incentives to ensure appropriate risk management practices.
  • Exploring the expansion of U.S. Food and Drug Administration authority to add to post-market monitoring capabilities.
  • Examining relevant liability laws and standards.
  • Supporting appropriate payment mechanisms.

For more information about ACR’s AI-related programs and initiatives, visit the ACR Data Science Institute website. For questions about AI oversight policy, contact Michael Peters, ACR Senior Director, Government Affairs.

Related ACR News

  • Radiology’s Fight Against Prior Authorization Delays

    ACR is leading national efforts to make prior authorization more efficient and clinically appropriate while reducing the administrative burden and supporting national legislation.

    Read more
  • ACR Supports Medicaid Coverage of Lung Cancer Screening

    ACR-backed bill would mandate Medicaid lung cancer screening, expand cessation coverage, ban prior auth—aiming to save lives and reduce disparities.

    Read more
  • AI-Powered Learning for Smarter Radiology Education

    This article discusses the role of AI in radiology and how AI errors can be reframed as enhanced learning opportunities in radiology education.

    Read more