Stanford
University
  • Stanford Home
  • Maps & Directions
  • Search Stanford
  • Emergency Info
  • Terms of Use
  • Privacy
  • Copyright
  • Trademarks
  • Non-Discrimination
  • Accessibility
© Stanford University.  Stanford, California 94305.
Michelle M. Mello's Testimony Before the U.S. House Committee on Energy and Commerce Health Subcommittee | Stanford HAI
Skip to content
  • About

    • About
    • People
    • Get Involved with HAI
    • Support HAI
    • Subscribe to Email
  • Research

    • Research
    • Fellowship Programs
    • Grants
    • Student Affinity Groups
    • Centers & Labs
    • Research Publications
    • Research Partners
  • Education

    • Education
    • Executive and Professional Education
    • Government and Policymakers
    • K-12
    • Stanford Students
  • Policy

    • Policy
    • Policy Publications
    • Policymaker Education
    • Student Opportunities
  • AI Index

    • AI Index
    • AI Index Report
    • Global Vibrancy Tool
    • People
  • News
  • Events
  • Industry
  • Centers & Labs

Stay Up To Date

Get the latest news, advances in research, policy work, and education program updates from HAI in your inbox weekly.

Sign Up For Latest News

Navigate
  • About
  • Events
  • AI Glossary
  • Careers
  • Search
Participate
  • Get Involved
  • Support HAI
  • Contact Us
policyTestimony

Michelle M. Mello's Testimony Before the U.S. House Committee on Energy and Commerce Health Subcommittee

Date
September 02, 2025
Topics
Healthcare
Regulation, Policy, Governance
Read Paper
abstract

In this testimony presented to the U.S. House Committee on Energy and Commerce’s Subcommittee on Health hearing titled “Examining Opportunities to Advance American Health Care through the Use of Artificial Intelligence Technologies,” Michelle M. Mello calls for policy changes that will promote effective integration of AI tools into healthcare by strengthening trust.

In collaboration with

Executive Summary

Although poorly designed regulation can hinder innovation, the government has a critical role to play in ensuring the conditions for innovation to translate into actual adoption of healthcare AI. In the U.S. market today, the key challenge isn’t stunted innovation — it’s low uptake of AI innovation. A major reason adoption lags behind innovation and interest in AI is a foundational trust deficit. There are four areas where experts agree policy changes could promote AI adoption by building confidence in its performance:

  1. Ensure the entities that develop and use AI adequately assess, disclose, and mitigate the risks of these tools. Healthcare organizations and health insurers should be required to show they have an AI governance process in place that meets certain standards. AI developers should be required to document and disclose key pieces of information about their products’ design and performance.

  2. Support independent research on how AI tools perform in practice. Such research can help healthcare organizations and insurers answer important questions about where investments in AI solutions can generate the greatest benefit and minimize potential risks. It also ensures that this knowledge is disseminated broadly.

  3. Modify healthcare reimbursement policies to better support adoption and monitoring of effective AI tools. Many AI tools will not save healthcare organizations money, and monitoring them properly can be costly.

  4. Address shortcomings in the Food and Drug Administration’s statutory framework to make the agency a more constructive partner in AI development and adoption. In some areas, the agency’s authority doesn’t go far enough; in others, it burdens developers with an antiquated regulatory framework that fails to anticipate the AI revolution.


A recording of Michelle M. Mello's testimony and the full committee hearing is available on YouTube.

Read Paper
Share
Link copied to clipboard!
Authors
  • Michelle Mello
    Michelle Mello

Related Publications

Designing Loyalty: AI Agents and Conflicts of Interest
Ella Genasci Smith, Victor Y. Wu, Jennifer King
Quick ReadAug 25, 2026
Issue Brief

This brief examines the conflict-of-interest risks posed by AI agents and calls for imposing a duty of loyalty on developers and deployers.

Issue Brief

Designing Loyalty: AI Agents and Conflicts of Interest

Ella Genasci Smith, Victor Y. Wu, Jennifer King
Privacy, Safety, SecurityRegulation, Policy, GovernanceIndustry, InnovationQuick ReadAug 25

This brief examines the conflict-of-interest risks posed by AI agents and calls for imposing a duty of loyalty on developers and deployers.

Regulating Data Brokers in the Age of AI: A California Case Study
Anna-Maria Gueorguieva, Jennifer King, Apoorva Panidapu, Daniel E. Ho
Quick ReadAug 11, 2026
Policy Brief

This brief assesses data broker compliance with California privacy laws and calls for more stringent consumer protections.

Policy Brief

Regulating Data Brokers in the Age of AI: A California Case Study

Anna-Maria Gueorguieva, Jennifer King, Apoorva Panidapu, Daniel E. Ho
Privacy, Safety, SecurityRegulation, Policy, GovernanceQuick ReadAug 11

This brief assesses data broker compliance with California privacy laws and calls for more stringent consumer protections.

The World Model and Spatial Intelligence Era: Governing AI Beyond Language
Daniel Zhang, Russell Wald, Ehsan Adeli, Elena Cryst, Daniel E. Ho, Caroline Meinhardt, Jiajun Wu, Amy Zegart, Fei-Fei Li
Quick ReadJul 27, 2026
Issue Brief

This brief highlights the emergence of world models and outlines a first-of-its-kind governance and policy agenda for the technology.

Issue Brief

The World Model and Spatial Intelligence Era: Governing AI Beyond Language

Daniel Zhang, Russell Wald, Ehsan Adeli, Elena Cryst, Daniel E. Ho, Caroline Meinhardt, Jiajun Wu, Amy Zegart, Fei-Fei Li
Foundation ModelsRegulation, Policy, GovernanceIndustry, InnovationSpatial IntelligenceQuick ReadJul 27

This brief highlights the emergence of world models and outlines a first-of-its-kind governance and policy agenda for the technology.

Operationalizing Real-Time Monitoring of Clinical AI
Zhongnan Fang, Lina Cheuy, Hye Sun Na, Akshay Chaudhari, David B. Larson
Quick ReadMay 14, 2026
Policy Brief

This brief demonstrates how real-time monitoring can address critical gaps in the oversight of radiological AI tools.

Policy Brief

Operationalizing Real-Time Monitoring of Clinical AI

Zhongnan Fang, Lina Cheuy, Hye Sun Na, Akshay Chaudhari, David B. Larson
HealthcareRegulation, Policy, GovernanceQuick ReadMay 14

This brief demonstrates how real-time monitoring can address critical gaps in the oversight of radiological AI tools.