Who Accredits and Certifies AI in Healthcare?

Artificial intelligence is reshaping healthcare — from medical imaging and diagnostics to administrative automation and predictive risk modeling. These innovations promise improved outcomes, greater efficiency and personalized care. At the same time, clinicians, patients and regulators are asking a pivotal question — who verifies that these AI systems are safe, ethical and fit for clinical use?

Because AI can influence diagnoses, therapy decisions and patient safety, accreditation and certification frameworks help ensure that developers and users follow transparent processes, responsible data practices and strong governance.

Why Healthcare AI Accreditation Matters

Healthcare AI systems are often high-stakes technologies. Even a well-designed model can produce biased outputs, misinterpret clinical data or behave unpredictably in unfamiliar settings.

Some AI applications are classified as high-risk under evolving regulation, meaning they carry significant implications for patient outcomes and privacy. Accreditation and certification serve several functions:

• Increase trust and adoption: Health systems, payers and procurement teams often look for third-party validation when evaluating AI tools for clinical use. Accreditation supports confidence that governance, oversight and performance evaluation processes are in place.
• Develop shared standards: Accreditation frameworks help align developers and healthcare organizations around consistent practices for governance, risk management, transparency and performance monitoring, reducing fragmentation across implementations.
• Mitigate risks: By documenting and auditing development, deployment and operational processes, accreditation supports early identification of risks such as algorithmic bias, workflow misalignment or data security gaps.
• Support clinical and organizational readiness: Beyond technical performance, accreditation addresses factors such as clinician oversight, escalation pathways, staff training and internal accountability structures that influence real-world AI use.
• Align regulations and procurement: Accredited organizations are often better positioned to adapt to emerging regulatory expectations and meet buyer requirements that reference independent standards.
• Encourage sustainable AI programs: Standardized governance supports long-term scalability, enabling healthcare organizations to expand AI use responsibly while maintaining consistency, safety and public trust.

Organizations Involved in Accrediting and Certifying AI in Healthcare

As interest grows around accrediting or certifying AI in healthcare, several organizations and initiatives have emerged to define standards, frameworks and formal accreditation pathways for healthcare-focused AI.

URAC

URAC is a Washington, D.C.–based nonprofit organization that has been accrediting healthcare organizations since 1990. Its mission centers on advancing healthcare quality through independent, third-party validation. Its accreditation and certification programs span hospitals, health plans, pharmacies, telehealth, remote patient monitoring and other healthcare services, using evidence-based standards developed in collaboration with clinical experts, industry stakeholders and advisory councils.

In response to the growing role of AI in clinical and operational settings, URAC launched the nation’s first accreditation process for AI in healthcare. The program applies URAC’s long-standing quality framework to healthcare AI, enabling both AI developers and healthcare organizations to demonstrate responsible governance, ethical use, transparency and risk oversight. The process emphasizes flexibility, allowing organizations to meet rigorous standards without prescribing a single implementation approach.

URAC’s accreditation model is a collaborative and educational process, supported by clinical reviewers, structured guidance and ongoing resources. Independent validation, external data review partnerships and continuous improvement principles are core components of its methodology. By extending its quality standards into healthcare AI, URAC provides a structured pathway for organizations seeking formal recognition of accountable AI practices within an evolving regulatory and technological environment.

Coalition for Health AI

The Coalition for Health AI (CHAI) is a nonprofit, public–private partnership focused on advancing responsible AI across the healthcare ecosystem. Its mission centers on guiding the development, deployment and oversight of healthcare AI through collaboration among health systems, industry, government, academia and patient communities. CHAI operates as a convener and consensus-builder, bringing together thousands of organizations and individual experts to align on shared principles for safe, transparent and effective AI use in healthcare.

CHAI’s work centers around four core pillars — convening stakeholders, developing best-practice frameworks, supporting certification-related efforts and educating the health sector. Through multidisciplinary working groups, CHAI facilitates structured dialogue on governance, accountability, clinical integration and trust in AI-enabled technologies. These efforts contribute to guidance that organizations can use to evaluate and operationalize responsible AI practices.

Although CHAI does not function as a traditional accrediting body, its frameworks and guidelines influence how healthcare organizations and technology developers approach AI governance and readiness. Open, consensus-driven participation allows input from health systems, startups, advocacy groups and standards organizations, helping ensure broad applicability across care settings. By serving as a trusted source of shared guidance, CHAI plays a central role in shaping the foundations that support accreditation, certification and oversight of AI in healthcare.

Institute for AI Governance in Healthcare

The Institute for AI Governance in Healthcare (IAIGH) is an organization dedicated to establishing governance standards that support the responsible use of AI in healthcare. IAIGH upholds and develops the Healthcare AI Governance Standard (HAIGS), a healthcare-specific framework designed to guide organizations in managing AI systems used in patient care with a focus on safety, transparency, equity and operational consistency.

HAIGS aligns with widely recognized regulatory and technical frameworks, including ISO 42001, the European Union Artificial Intelligence Act, the National Institute of Standards and Technology Artificial Intelligence Risk Management Framework (NIST AI RMF), the Health Insurance Portability and Accountability Act (HIPAA) and related information security standards. By adding a clinical governance layer, HAIGS addresses oversight, risk assessment and implementation considerations unique to healthcare environments while allowing organizations to leverage existing compliance efforts.

IAIGH supports organizations through the full HAIGS adoption life cycle, providing governance tools, documentation templates, training resources and expert guidance. The certification process includes an external audit to verify alignment with the standard and demonstrate organizational commitment to ethical and accountable AI governance. Ongoing support and maintenance resources help organizations adapt as technologies and regulatory expectations evolve. Through standard development, certification and education, IAIGH contributes a structured pathway for healthcare organizations seeking formal recognition of responsible AI governance.

The Difference Between Regulation and Accreditation

Regulation comes from public authorities such as the U.S. Food and Drug Administration (FDA) or the European Union under the EU Artificial Intelligence Act. These authorities set legally binding requirements, including conformity assessments, clinical validation, safety testing and market entry approvals for high-risk medical AI systems. Regulatory frameworks primarily focus on minimum safety and performance standards, which are enforceable by law and carry penalties for noncompliance.

Accreditation, in contrast, is voluntary and offered by independent organizations, such as URAC, CHAI and IAIGH. Accreditation evaluates an organization’s processes, governance, risk management, transparency and adherence to quality standards. While it does not replace legal compliance, accreditation demonstrates proactive commitment to best practices, independent validation and accountability.

Accreditation also encourages continuous improvement through structured reviews, audits and benchmarking, enabling organizations to maintain safe and ethical AI operations that exceed minimum regulatory requirements. For healthcare providers, AI developers and stakeholders, accreditation can support market adoption, stakeholder trust and readiness for future regulatory changes, providing a practical pathway to align innovation with accountability in a rapidly evolving technological and legal landscape.

What Developers and Providers Should Consider

Healthcare organizations developing or deploying AI solutions can benefit from a structured approach to accreditation and certification. Several strategic considerations can support responsible adoption and long-term operational success:

• Early alignment with independent standards: Integrate governance, transparency and risk-management practices that align with established frameworks, such as URAC, HAIGS or other recognized guidelines. Early alignment can streamline certification and build stakeholder confidence.
• External certification evaluation: Consider whether pursuing formal certification adds value for buyers, partners, regulators or patients. Certification can signal organizational commitment to ethical, accountable and clinically sound AI practices.
• Regulatory alignment preparation: Map accreditation efforts to evolving regulations, including the EU’s Artificial Intelligence Act and U.S. FDA guidance for Software as a Medical Device, to ensure readiness for future compliance requirements.
• Continuous performance monitoring: Establish procedures to track AI performance over time, monitor for data drift and validate outcomes against intended clinical use. Continuous oversight reduces operational risks and supports safer deployment.
• Stakeholder engagement and training: Educate clinicians, staff and patients on AI capabilities, limitations and governance policies. Training promotes responsible use, facilitates integration into clinical workflows and strengthens trust in AI tools.
• Documentation and explainability: Maintain detailed records of model design, training data, evaluation results and decision-making processes. Transparency supports accountability and satisfies expectations from accrediting bodies.
• Thoughtful AI integration into clinical workflows: Plan for interoperability, human oversight and alert management to maximize efficiency and safety while minimizing disruption to existing care processes.
• Ethical and equity considerations: Assess algorithms for fairness, bias and accessibility to ensure AI supports equitable patient outcomes and aligns with recognized ethical standards.

A Rapidly Maturing Ecosystem

The accreditation and certification landscape for AI in healthcare continues to evolve as AI becomes increasingly integrated into clinical and operational workflows. Independent accreditation programs and governance frameworks are emerging to address shared concerns around transparency, data stewardship, risk management and responsible deployment.

While formal regulatory oversight remains the responsibility of government agencies, third-party organizations, nonprofit initiatives and standards bodies contribute structured approaches for evaluating how healthcare AI systems are developed and used.

For AI developers and healthcare organizations alike, awareness of these accreditation and certification pathways supports informed decision-making and alignment with evolving expectations for safety, accountability and long-term sustainability in healthcare technology.