Who Accredits or Certifies AI in Health Care?

In This Article

Artificial intelligence (AI) is transforming health care. From diagnostic imaging that identifies diseases earlier to administrative systems that reduce paperwork burdens, AI tools promise efficiency and improved outcomes. However, leaders adopting these technologies face a critical challenge in ensuring the AI systems they implement are safe, effective and trustworthy. With fragmented regulations and no single governing authority, the public and organizations alike are unclear on AI standards. We partnered with URAC to explain why accreditation is the most reliable way to verify AI quality. 

Navigating the Evolving Regulatory Landscape for Health AI

While agencies like the Food and Drug Administration and the Department of Health and Human Services play roles in AI oversight, currently fragmented federal oversight means there is no single body governing all health AI applications. The FDA regulates AI-enabled medical devices that diagnose or treat conditions, but many AI tools fall outside this definition. 

The HHS has introduced recent proposals to expand oversight of clinical decision support software and algorithms that affect patient care. However, these efforts are still evolving, and significant gaps remain. 

How States Are Stepping in to Shape Policy

In the absence of comprehensive federal law, states are introducing legislation to govern AI in health care. These bills address data privacy protections, algorithmic fairness requirements and transparency obligations. Some states mandate impact assessments before deploying AI in clinical settings, while others require disclosure when AI influences medical decisions. The variability in state approaches means your organization may face different compliance obligations depending on where you operate. This underscores the need for a voluntary standard that demonstrates your commitment to quality.

Why Independent Accreditation Is Essential for Safe AI

AI in health care is not a single technology but a collection of machine learning technologies designed for specific clinical and administrative tasks. Common applications include:

• Diagnostic imaging algorithms that detect abnormalities in scans.
• Clinical decision support tools that help physicians identify treatment options.
• Administrative AI that automates billing, schedules appointments and manages records.
• Predictive analytics that assess patient risk and forecast disease progression.

Each application carries unique risks that require verification. The diversity of AI applications means your organization needs a comprehensive framework to evaluate safety and effectiveness. Accreditation verifies that your AI systems meet rigorous standards for performance, fairness and ongoing monitoring.

Confronting the Hidden Risk of Algorithmic Bias

AI models learn from historical data, and when that data reflects existing health care disparities, the algorithms can perpetuate inequitable outcomes. The risk of algorithmic bias is particularly concerning in clinical settings where diagnostic or treatment recommendations may disadvantage certain patient populations. 

Biased training data can lead to less accurate predictions for underrepresented groups, resulting in delayed diagnoses or inappropriate care. Accreditation processes evaluate how AI developers identify and mitigate bias in their models, ensuring fairness across diverse patient populations.

Preventing a Decline in AI Performance and Data Quality

AI accuracy is not static. Research shows that AI systems can experience a significant decline when data quality degrades over time or when patient populations shift in ways the model did not anticipate. Without continuous evaluation, an algorithm that performed well during initial validation may deliver unreliable results later. Accreditation frameworks require ongoing monitoring and periodic revalidation to ensure AI tools maintain their accuracy throughout their life cycle.

Building a Trust Framework for Your Hospital or Practice

Implementing AI in clinical settings requires buy-in from physicians, staff and patients who may be skeptical of automated decision-making. AI accreditation serves as a practical bridge between innovation and patient safety by establishing formal processes for evaluating and monitoring AI tools. It signals to stakeholders that your AI implementations have been independently verified against recognized standards. External validation can ease concerns among clinicians and reassure patients that their care involves appropriate human oversight.

How URAC Is Setting the Standard for AI Accreditation

URAC is a recognized leader in health care accreditation, having recognized more than 1,000 organizations since 1990. It has developed accreditation standards specifically designed to evaluate AI systems across multiple dimensions, including clinical validity, data quality, algorithmic transparency and ongoing performance monitoring. URAC’s approach examines how AI tools are developed, validated and integrated into clinical workflows, with external reviewers assessing compliance against comprehensive criteria.

The organization’s AI accreditation framework evaluates whether algorithms are trained on representative data, whether performance metrics are appropriate for the intended use and whether monitoring systems detect degradation in real time. 

Accreditation timelines vary, but they can take around four to five months, while awards are for a period of three years. By earning URAC accreditation, your organization demonstrates that its AI implementations meet rigorous independent standards for safety and effectiveness.

Frequently Asked Questions About AI Accreditation

This is a fast-evolving area, but here are some answers to common questions. 

What is the difference between accreditation and certification?

Accreditation evaluates an organization’s processes and systems to ensure it meets quality standards for developing, validating and monitoring AI tools. Certification typically applies to individual products or professionals. In the AI context, accreditation examines your entire governance framework, while certification might apply to a single algorithm.

How can you verify if an AI tool is accredited?

The FDA maintains a public list of approved AI-enabled medical devices that have received marketing authorization. For organizational accreditation, check directly with accrediting bodies like URAC to confirm whether a health care organization has earned AI accreditation.

Is a human still involved in AI-driven medical decisions?

Most health care AI systems function as decision support tools rather than autonomous decision-makers. Physicians retain ultimate authority over patient care. Accreditation standards require human oversight mechanisms to ensure clinicians can review and override AI recommendations when warranted.

Preparing Your Organization for an AI-Enabled Future

AI adoption in health care is accelerating, and the regulatory landscape remains complex and fragmented. Organizations committed to patient safety can no longer rely solely on incomplete federal oversight or inconsistent state requirements. Independent accreditation from proven bodies offers the clearest path forward, providing verification that your AI systems meet rigorous standards for quality, fairness and ongoing performance. As you evaluate AI investments, prioritizing accredited solutions demonstrates the due diligence your patients and stakeholders expect.