AHA Statement of Record for Senate HELP Committee Hearing on AI in Health Care

Statement

of the

American Hospital Association

for the

Committee on Health, Education, Labor and Pensions

of the

United States Senate

“AI's Potential to Support Patients, Workers, Children, and Families”

October 9, 2025

On behalf of our nearly 5,000 member hospitals and health systems and other health care organizations, as well as our clinician partners — including more than 270,000 affiliated physicians, 2 million nurses and other caregivers — the American Hospital Association (AHA) appreciates the opportunity to submit this statement for the record to the Committee on Health, Education, Labor and Pensions hearing, “AI’s Potential to Support Patients, Workers, Children, and Families.”

HOSPITALS AND HEALTH SYSTEMS ARE LEVERAGING AI TO TRANSFORM CARE DELIVERY

Although artificial intelligence (AI) has been leveraged in health care for decades, increased data storage capacity expanded computing power and prevalence of large complex data sets, or “big data,” are driving rapid advancement of innovative AI applications in hospitals and health systems across the country.

We have seen exponential growth in the applications of AI in health care, as demonstrated by the number of AI tools that have been approved by the Food and Drug Administration (FDA). Over 1,200 AI-enabled medical devices have been approved by the FDA, the majority of which were approved in the last three years.¹

Hospitals recognize that AI tools hold tremendous promise to alleviate administrative burden and enhance clinical care. Among other benefits, these innovations can improve workflow, enhance the overall patient experience by reducing wait times and support timely medical interventions.

A few of the many examples of promising AI applications in hospitals include:

• Diagnostic imaging. AI is augmenting X-ray, MRI, CT scan and other image reviews in hospitals. Certain AI tools can detect and alert clinicians to subtle changes in tissue images, which is crucial for early disease detection. For example, some hospitals are leveraging AI tools to detect diabetic retinopathy in patients — a condition that can lead to blindness without early intervention.
• Ambient listening tools. These tools assist with clinical documentation in electronic health records by automatically transcribing and formatting clinical notes — tasks that providers often have to finish outside of clinic hours. By reducing the amount of time required for notetaking, clinicians can better focus on direct patient care and hospitals can reduce physician burnout.
• Scheduling. AI tools are also helping to optimize patient and staff scheduling by analyzing historical utilization patterns to project future demand.

Hospitals and health systems are just scratching the surface of potential use cases and are eager to explore new ways these tools can support the patients and communities they serve.

ENSURING SAFE AND EFFECTIVE USE OF AI

Although there are clear benefits and opportunities for AI to drive efficiencies in hospitals and health systems, there are also some potential risks.

For example, commercial insurer use of AI to determine disposition of claims and prior authorizations has exacerbated issues with inappropriate denials. A recent American Medical Association survey found that 62% of doctors think that payer use of AI is increasing denials for medically necessary care.² To mitigate this, as we recently commented to the Department of Health and Human Services, clinicians — not just AI tools — must be included in the decision loop for any recommendations of partial or full denial of requested items or services.³ While the use of AI tools to more quickly process prior authorizations is not inherently problematic, it is imperative that any recommendation to deny care, whether it is or is not AI-generated, is independently reviewed by a clinician. Further, human reviewers must have the requisite training and expertise to engage in an informed medical decision about a patient’s condition and the proposed treatment plan.

Additionally, AI systems rely on large data sets to maximize their predictive power. However, aggregating large data sets may also pose unique cybersecurity vulnerabilities. With the rise in protected health information data breaches related to third-party vendors, it is essential that entities not covered by HIPAA (including certain AI vendors that may not meet the definition for covered entities or business associates) be subject to the same privacy and security standards that health care providers must follow.

Finally, hospitals and health systems continually work to ensure they can fully assess the strengths and limitations of all AI models they use. The “black box” nature of many AI systems can make it more challenging for hospitals and health systems to identify flaws in models that may affect the accuracy and validity of an AI tool’s analyses and recommendations. There are many reports of certain AI tools producing “hallucinations” or false results based on flaws in model design or biases in underlying data. This underscores the importance of ongoing testing to maintain AI model validity. Voluntary pre-market and post-deployment standards that are developed with the input of stakeholders across the health care ecosystem can support the safe and effective use of these tools.

SUMMARY

AI tools offer tremendous promise for improved patient outcomes while enhancing efficiency and improving the quality of the work lives of dedicated health care workers. At the same time, this breakthrough also poses novel challenges. We look forward to working with Congress on policy solutions to balance the need for flexibility to enable innovation with the need to ensure the safe and effective use of AI.