Hold Public Hearings on AI Governance in Federal Healthcare Before Further Authorization

I am writing to urge you to demand public hearings on artificial intelligence governance in federal healthcare systems before any additional authorizations move forward. The rapid deployment of AI technologies in healthcare decision-making affects millions of Americans, yet there has been insufficient public scrutiny of how these systems operate, who oversees them, and what safeguards exist to protect patients. Federal healthcare programs including Medicare, Medicaid, and Veterans Affairs serve over 140 million Americans. These constituents deserve transparency about how AI algorithms may influence their care decisions, coverage determinations, and treatment options. Without proper oversight frameworks established through public hearings, we risk implementing systems that could deny necessary care, perpetuate existing healthcare disparities, or make life-altering decisions without adequate human review. The current approach of authorizing AI systems without comprehensive public input mirrors the pattern we have seen in other policy areas where implementation races ahead of accountability. Public hearings would allow medical professionals, patient advocates, technology experts, and affected communities to testify about necessary safeguards. These sessions should examine algorithmic bias in diagnosis and treatment recommendations, data privacy protections, liability frameworks when AI systems fail, and the right of patients to understand and appeal AI-influenced decisions. I am specifically requesting that you advocate for mandatory public hearings in the relevant committees before any new AI healthcare authorizations receive votes. These hearings should include testimony from independent researchers, not just technology vendors and agency officials. The stakes are too high to proceed without this essential democratic process. Healthcare decisions are deeply personal and often determine quality of life or survival. Constituents need assurance that AI systems serving them have been subjected to rigorous public examination and that robust governance structures are in place before, not after, widespread implementation.