House Health Hearing - AI in Healthcare - 9 3 2025
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The House Health Subcommittee convened on September 3, 2025, for a hearing titled "Examining Opportunities to Advance American Health Care through the Use of Artificial Intelligence Technologies". The session, which featured testimony from five experts in health policy and AI technology, highlighted the transformative potential of artificial intelligence (AI) across the healthcare sector, while also emphasizing critical concerns regarding oversight, patient safety, and equitable access. Policy experts and industry leaders acknowledged the rapid expansion of AI applications, stressing the need for Congress to keep pace with these advancements to ensure responsible and beneficial integration into daily American healthcare.
Key Themes and Major Issues Discussed
The three-hour hearing illuminated several overarching themes, ranging from the immediate practical applications of AI to the long-term ethical and regulatory challenges.
I. Opportunities and Benefits of AI in Healthcare
Witnesses and committee members outlined numerous ways AI is currently, or could soon, enhance healthcare delivery and outcomes:
- Reduction of Administrative Burdens: AI and machine learning are increasingly deployed to streamline time-consuming and costly administrative tasks, which can significantly reduce documentation time for physicians (e.g., by roughly one-third in some cases for post-visit records). This frees up clinicians to spend more time directly with patients, enhancing the provider-patient relationship. TJ Parker, founder of General Medicine, noted that many new healthcare AI startups are focused on administrative tools like AI scribes, prior authorization automation, and AI phone calls, recognizing their downstream impact on customers.
- Improved Patient Experience and Outcomes:
- Price Transparency: General Medicine, for example, utilizes AI and large language models to analyze complex insurance benefit documents (80-100 pages) and open pricing files to provide clear, upfront prices for any service (procedures, labs, imaging, prescriptions). This aims to eliminate the "opacity and quagmire of complexity" patients face in understanding healthcare costs.
- Proactive and Personalized Care Plans: AI enables the creation of comprehensive, personalized, and actionable care plans post-visit by pulling together medical history, labs, and prescriptions to flag missing preventative care or follow-up tasks. This empowers patients in collaboration with providers to take informed control of their health. Andrew Toy, CEO of Clover Health, highlighted how AI helps doctors personalize care beyond generic population management, leading to earlier identification and treatment of diseases.
- Expanded Access to Sophisticated Care: AI can democratize access to advanced data systems, previously limited to large hospital networks, for independent doctors, especially in rural and underserved areas. This can mitigate misdiagnoses, prevent duplicate tests, and reduce dangerous delays.
- Accelerated Biomedical Research and Drug Development:
- Drug Discovery: Pharmaceutical companies are leveraging AI to improve scientific research, develop life-saving treatments, and expedite clinical trials to bring medicines to market quicker. AI can rapidly analyze vast chemical, genomic, and proteomic data to identify promising drug candidates, predicting molecular behavior and target-binding affinities with greater speed and accuracy.
- Clinical Trial Recruitment: Researchers at the National Institutes of Health (NIH) developed an AI algorithm, TrialGPT, which modernizes the process of matching potential clinical trial volunteers to suitable trials, cutting administrative time by 40% while maintaining accuracy. This can help overcome challenges in clinical trial recruitment, particularly for diverse populations.
- Federal Agency Efficiency:
- FDA Review Process: The FDA uses a generative AI tool called "Elsa" to aid staff in functions like clinical protocol reviews and scientific evaluations, drastically shortening the time needed for some tasks from days to minutes.
- CMS Fraud Detection: The CMS Innovation Center is utilizing AI and machine learning to identify waste, fraud, and abuse in federal healthcare systems, aiming to protect taxpayer spending.
- Diagnostics (Pathology and Radiology Focus):
- Earlier Disease Detection: AI-powered tools are demonstrating significant impact in diagnostics. Viz.ai, for example, uses AI to automatically analyze CT scans to identify life-threatening conditions like stroke, alerting medical teams in real-time and reducing treatment times by over 30 minutes and hospital stays by three days. This technology is scaled across 1,800 hospitals nationwide, including critical access hospitals in rural America.
- Viz.ai has applied similar models to other serious conditions:
- Hypertrophic Cardiomyopathy (HCM): An AI tool cut time-to-diagnosis from years to just three months, aiding in identifying this disease which can lead to sudden cardiac death if undiagnosed.
- Pulmonary Embolism (PE): An AI platform reduced time-to-treatment from hours to six minutes, significantly reducing in-hospital deaths.
- Brain Aneurysms: AI increases detection on routine imaging for aneurysms, allowing for earlier treatment before rupture.
- Pre-reading and Triage: Dr. Ibrahim envisioned a future where AI could triage imaging studies to identify high-risk conditions (e.g., high-risk stroke, life-threatening heart conditions), though he cautioned that a human would still be needed to solidify the diagnosis.
- Growth in FDA Approvals: There has been a significant increase in FDA-approved AI-enabled medical devices, from only six approvals in 2015 to 223 in 2023, with overwhelmingly radiological tools comprising nearly 1,000 approved applications by the FDA.
II. Risks and Concerns with AI in Healthcare
Despite the optimism, lawmakers and experts articulated profound concerns about the potential downsides and necessary safeguards:
- AI Should Assist, Not Replace Clinicians: A unanimous sentiment among witnesses and committee members was that AI tools are intended to assist, not replace, the clinical workforce. Human judgment must remain central to care.
- Foundational Trust Deficit and Oversight Gaps: There is a significant "foundational trust deficit" that hinders AI adoption. Most healthcare organizations and insurers do little vetting of AI tools before use or meaningful monitoring afterward, as the law does not require it. This lack of transparency and accountability from AI developers contributes to apprehension about risks.
- Patient Safety and Bias:
- Replicating Inequities: AI models risk amplifying existing health disparities if trained on incomplete or skewed data. Examples include algorithms underestimating the needs of Black patients by using healthcare costs as a proxy for health status, and dermatology tools misdiagnosing conditions on darker skin tones due to training primarily on lighter skin tones. Dr. Ibrahim stressed that it's not just the amount of data, but the correct and representative data that matters.
- Mental Health Risks: Unregulated direct-to-consumer chatbots pose significant dangers. Instances were cited where entertainment chatbots, some masquerading as psychologists, provided deceptive or harmful advice, including encouraging self-harm or validating violent thoughts. Children and adolescents are particularly vulnerable due to their developmental stage and lack of life experience to discern harmful advice. The American Psychological Association (APA) highlighted that such chatbots are often coded to be "unconditionally validating and reinforcing even harmful or unhealthy behaviors" to increase user engagement.
- AI in Prior Authorization: A major point of contention was the use of AI in prior authorization processes. Lawmakers expressed strong concerns that AI-powered systems are being leveraged by Medicare Advantage plans to deny patient prior authorization requests, boosting profits through "predictive denials" and limiting access to care. Michelle Mello, a health policy scholar from Stanford, warned that even with a human reviewer, they could be "primed" by AI to accept denials, effectively rubber-stamping machine decisions. Representative Landsman criticized the proposed CMS Wasteful and Inappropriate Service Reduction (WISeR) Model, which would use AI for prior authorization in traditional Medicare, citing the "perverse incentive" for companies to deny more claims for financial gain. Clover Health explicitly stated it does not use AI for utilization management or prior authorization denials, believing it's not ready for such applications.
- Data Privacy and Security: The reliance of AI on large quantities of data raises significant concerns about personal health information and data privacy breaches. The concept of "mental privacy," safeguarding biometric and neural information from wearables that can infer mental states without consent, was also introduced as a critical area for comprehensive data privacy legislation. The potential for de-anonymization of data in digital twin technology also prompted questions.
- Regulatory Framework Limitations: The FDA's current statutory authority, dating back to the Ford Administration, is seen as antiquated and not well-suited for evaluating constantly learning and evolving AI algorithms. Many AI tools that affect patient safety are not even subject to FDA jurisdiction.
- Physician Liability: The legal question of liability when AI provides an incorrect recommendation or a physician departs from AI advice, and something goes wrong, was raised as a complex issue that needs to be addressed. Developers often contract away responsibility and disclaim liability in licensing agreements, leaving hospitals and physicians "on the hook".
- Challenges for Rural Areas: While AI offers promise for rural communities, institutions with fewer resources, like rural hospitals and community health centers, are less likely to be able to conduct robust evaluations and thoughtful implementations of AI tools in the current environment. They also face issues with internet connectivity and lack access to modern EHR systems.
- Pediatric Care Disparity: A significant disparity exists in AI tool development for pediatric versus adult care. Of over 200 FDA-approved AI tools for medical imaging, only six are marketed for pediatric use. Children are not "little adults," and their physiological differences require specific, extensive, and representative data for AI models.
III. Policy Recommendations and the Regulatory Landscape
Experts provided several policy recommendations to foster responsible AI adoption:
- Establish Clear Regulatory Guardrails: A robust federal framework is needed to ensure safety and efficacy, including prohibiting the misrepresentation of AI as licensed professionals and mandating transparency and human oversight over clinical decisions.
- Modernize FDA Authority: Congress should empower the FDA with a modernized statutory framework to be a more constructive partner in AI development and adoption. This could involve new legislation similar to "21st Century Cures" to efficiently differentiate "snake oil" products from effective ones.
- Support Independent Research and AI Literacy: Significant federal investment in independent, longitudinal research is needed to understand AI's impacts on development and well-being. This should be coupled with comprehensive AI literacy education for the public and providers to enable safe and critical use of these tools.
- Improve Reimbursement Policies: Healthcare reimbursement policies, particularly through Medicare and Medicaid, should be adapted to support the adoption and monitoring of effective AI tools, especially for rural and underserved hospitals. A more permanent pathway for reimbursement of FDA-approved, software-based technologies (SaaS) is crucial.
- Mandate Institutional Governance: Healthcare organizations and insurers should be required to implement AI governance processes that meet certain standards, similar to Institutional Review Boards (IRBs) for human subjects research. Stanford's model, involving C-suite committees and interdisciplinary teams to evaluate clinical utility, financial sustainability, fairness, and ethical considerations, was cited as an example.
- Enhance Developer Transparency: Developers should be required to document and disclose key information about AI design and performance, potentially through standardized "model cards," to inform customers about product strengths and weaknesses.
- Comprehensive Data Privacy Legislation: A strong federal privacy law is essential, establishing a "right to mental privacy" to safeguard biometric and neural information.
- Protect Vulnerable Populations: Age-appropriate safeguards, limits on access to harmful content, and robust data protections are necessary for youth interacting with AI. Interventions like "disruptors" (periodic interruptions reminding users it's not human) and disincentivizing addictive coding practices in chatbots were suggested.
- Address Liability Concerns: The legal framework should consider shared liability between physicians, hospitals, and AI developers to ensure accountability when AI errors lead to patient harm. This may involve regulating disclaimers of liability in licensing agreements.
- Accelerate Interoperability: Congress should accelerate connectivity to the internet and enforce interoperability rules across federal and private healthcare systems to ensure AI tools have access to comprehensive patient data. This should be a voluntary framework incentivizing participation, not imposing rigid "one-size-fits-all" mandates.
Special Focus on Diagnostics (Pathology and Radiology)
The discussion highlighted the significant and evolving role of AI in diagnostic fields:
- Impact on Speed and Accuracy: AI's ability to analyze medical images (CTs, EKGs) quickly and accurately for conditions like stroke, hypertrophic cardiomyopathy, pulmonary embolism, and brain aneurysms was frequently cited as a life-saving application. This capability significantly reduces diagnosis and treatment times.
- Human-AI Collaboration: While AI can triage and identify high-risk conditions from imaging studies, the consensus remains that a human clinician is essential to solidify the diagnosis and make final treatment decisions. This "pre-reading" capability of AI helps physicians focus their attention where it's most needed.
- Challenges of Data Quality and Representation: For AI diagnostic tools to be effective, they require correct and representative training data. Concerns were raised about models trained on unrepresentative populations (e.g., Sub-Saharan African X-ray data for European patients) leading to false positives. This is particularly critical in specialized areas like pediatric radiology, where physiological differences mean AI tools developed for adults are often not appropriate or safe for children, and there's a significant disparity in available pediatric-specific AI tools.
- Financial and Training Considerations: While AI in radiology shows promise for revenue enhancement by allowing radiologists to review more scans, rural hospitals, despite the benefits, face challenges in adopting these tools due to costs beyond just purchase, including training radiologists on how to use and monitor the AI effectively.
- Interoperability and Data Sharing: Improved IT infrastructure for sharing radiological images and data across institutions, potentially through sharing "weights of algorithms" rather than individual patient data to maintain privacy, is crucial for advancing AI in diagnostics, especially for rarer conditions and pediatric populations where sample sizes are smaller.
In conclusion, the hearing underscored that AI is rapidly integrating into American healthcare, offering immense potential for efficiency, personalization, and improved outcomes, particularly in diagnostics and administrative tasks. However, realizing this potential necessitates proactive legislative and regulatory actions to address profound challenges related to oversight, patient safety, data privacy, and the ethical integration of AI with human clinical judgment, especially for vulnerable populations and in ensuring equitable access across all communities.
