Artificial intelligence is transforming healthcare. Hospitals, clinics, and healthcare technology companies are using AI to automate documentation, improve clinical workflows, support medical imaging, and enhance patient engagement. While these tools offer significant benefits, they also introduce new risks related to privacy, security, fairness, and patient safety.

Healthcare organizations often ask a simple question: How can we adopt AI responsibly while protecting patient data?

One of the best places to start is the NIST AI Risk Management Framework (AI RMF).

Developed by the National Institute of Standards and Technology (NIST), the framework provides practical guidance for identifying, assessing, and managing AI-related risks throughout an AI system's lifecycle. Although it is voluntary, it has become one of the most widely recognized frameworks for AI governance in the United States.

What Is the NIST AI Risk Management Framework?

The NIST AI Risk Management Framework is a structured approach that helps organizations build trustworthy AI systems.

Rather than focusing on a single technology or regulation, the framework encourages organizations to manage AI risks continuously. It recognizes that AI systems can affect privacy, security, transparency, fairness, reliability, and safety, and that these risks should be addressed throughout the planning, deployment, and operation of AI systems.

For healthcare organizations, the framework complements existing compliance efforts by providing a structured way to evaluate AI beyond traditional cybersecurity controls.

Why Healthcare Organizations Should Pay Attention

Healthcare organizations handle some of the most sensitive information in any industry.

When AI systems process Protected Health Information (PHI), organizations must consider not only HIPAA requirements but also broader governance issues such as data quality, model performance, human oversight, and accountability.

The NIST AI Risk Management Framework helps organizations answer important questions such as:

• Is this AI system appropriate for its intended purpose?
• What could go wrong if the AI makes an incorrect recommendation?
• Who is responsible for monitoring AI performance?
• How should AI risks be documented and managed over time?

These questions become increasingly important as AI is integrated into clinical and administrative workflows.

The Four Core Functions of the NIST AI Risk Management Framework

The framework is organized around four core functions that work together throughout the AI lifecycle.

1. Govern

Governance is the foundation of responsible AI.

Organizations should establish clear policies, assign accountability, and define roles for AI oversight. Leadership should ensure that AI systems align with organizational values, regulatory requirements, and patient safety goals.

Healthcare organizations should consider:

• Establishing an AI governance committee
• Defining approval processes for new AI tools
• Creating AI usage policies
• Assigning ownership for AI risk management
• Reviewing vendors before procurement

Strong governance helps ensure AI is adopted intentionally rather than informally across departments.

2. Map

The mapping function focuses on understanding the AI system and the context in which it operates.

Organizations should document:

• The purpose of the AI system
• The intended users
• The types of data being processed
• Expected benefits
• Potential harms
• Regulatory considerations
• Stakeholders affected by AI decisions

For example, an AI-powered documentation assistant presents different risks than an AI system used to support diagnostic decisions.

Understanding context allows organizations to identify risks early.

3. Measure

Once risks have been identified, organizations should evaluate how significant those risks are.

This includes assessing factors such as:

• Privacy risks
• Security vulnerabilities
• Accuracy
• Reliability
• Bias
• Explainability
• System robustness
• Human oversight

Healthcare organizations should also evaluate whether AI outputs remain accurate over time and whether system performance changes as clinical practices evolve.

Testing should not stop after implementation. Continuous monitoring is essential.

4. Manage

The final function focuses on taking action.

Organizations should prioritize risks, implement mitigation strategies, and monitor whether those controls remain effective.

Risk management activities may include:

• Updating organizational policies
• Improving access controls
• Enhancing employee training
• Strengthening vendor oversight
• Performing periodic audits
• Reviewing AI incidents
• Updating risk assessments after major system changes

AI governance should be viewed as an ongoing process rather than a one-time project.

How the Framework Supports HIPAA Compliance

The NIST AI Risk Management Framework is not a HIPAA compliance framework.

Instead, it complements HIPAA by helping organizations address AI-specific risks that may not be explicitly covered by traditional privacy and security assessments.

For example, while HIPAA focuses on protecting electronic Protected Health Information through administrative, physical, and technical safeguards, the NIST framework encourages organizations to also evaluate:

• AI decision quality
• Human oversight
• Model transparency
• Fairness
• System reliability
• Organizational accountability

Using both approaches together creates a more comprehensive AI governance program.

Applying the Framework to AI Procurement

Before purchasing an AI solution, healthcare organizations should ask vendors questions such as:

• How is patient data protected?
• Does the vendor offer a Business Associate Agreement if PHI is involved?
• Is customer data used to train AI models?
• What security controls are available?
• How is system performance monitored?
• How are security incidents reported?
• Does the vendor use subcontractors?
• What documentation supports ongoing risk management?

Vendor evaluations become much more effective when guided by a structured framework.

Common AI Risks the Framework Helps Address

Healthcare organizations frequently encounter challenges such as:

• Unauthorized use of AI tools
• Data privacy concerns
• AI hallucinations
• Bias in AI-generated recommendations
• Lack of transparency
• Poor documentation
• Weak governance
• Inadequate human review
• Vendor management gaps

The framework encourages organizations to evaluate each of these risks before they affect patient care or regulatory compliance.

Best Practices for Healthcare Organizations

Organizations implementing AI should consider the following practices:

• Maintain an inventory of approved AI systems
• Conduct AI-specific risk assessments
• Review vendors before deployment
• Establish clear AI governance policies
• Train employees on acceptable AI use
• Monitor AI performance regularly
• Document risk management activities
• Periodically reassess AI systems as technology evolves

These practices support stronger governance regardless of which AI platform is being used.

Final Thoughts

Artificial intelligence offers enormous opportunities to improve healthcare, but innovation should never come at the expense of patient trust.

The NIST AI Risk Management Framework provides healthcare organizations with a practical roadmap for identifying, evaluating, and managing AI risks throughout the entire lifecycle of an AI system. By combining strong governance, continuous monitoring, careful vendor evaluation, and thoughtful risk management, organizations can adopt AI more confidently while protecting patient information and supporting high-quality care.

Although the framework is voluntary, many healthcare organizations view it as a valuable complement to HIPAA compliance and broader cybersecurity programs. As AI adoption continues to grow, organizations that invest in structured AI governance will be better positioned to manage risk, build trust, and adapt to an evolving regulatory landscape.