Healthcare AI Policy Template: What Should Be Included?
- Jun 5
- 6 min read
Artificial intelligence is now being used across healthcare, pharma, digital health, and life sciences. Teams are using AI to support documentation, analytics, patient engagement, research, operations, clinical trial workflows, and administrative tasks.
But in many organizations, AI use is outpacing policy.
That creates risk.
A healthcare AI policy helps organizations define how AI tools can be used, what safeguards are required, who is accountable, and what oversight is needed before deployment. Without a clear policy, AI adoption can become inconsistent, fragmented, and difficult to govern.
For leaders, the goal is not to block the use of AI. The goal is to create clear guardrails that allow teams to innovate responsibly.
Why Healthcare Organizations Need an AI Policy
AI tools can affect patient safety, privacy, clinical decision-making, research integrity, health equity, cybersecurity, and organizational trust. Even tools that appear low risk can create exposure if teams enter protected health information into unapproved systems, rely on inaccurate outputs, or use AI-generated content without review.
A healthcare AI policy helps organizations address questions such as:
Which AI tools are approved for use?
What types of AI use are prohibited?
Can staff enter patient, confidential, or proprietary data into AI tools?
Who reviews new AI tools before adoption?
What level of human oversight is required?
How should AI outputs be validated?
How are risks, incidents, or concerns escalated?
Who is accountable for monitoring AI performance?
Clear policy is especially important as generative AI becomes easier for employees to access. Shadow AI can emerge quickly when staff use unapproved tools without guidance, training, or oversight.
Start With Scope and Definitions
A strong AI policy should begin by defining what the policy covers.
Organizations should clarify whether the policy applies to:
generative AI tools
predictive models
clinical decision support tools
AI-enabled medical devices
vendor platforms with embedded AI
automation tools
analytics platforms
research or clinical trial AI tools
patient-facing AI applications
internally developed AI systems
The policy should also define key terms such as artificial intelligence, machine learning, generative AI, high-risk AI use, human oversight, protected health information, and approved tools.
Clear definitions reduce confusion and help teams understand when the policy applies.
Define Approved and Prohibited Uses
An AI policy should give teams practical guidance on what is allowed and what is not.
Approved uses may include activities such as internal brainstorming, administrative support, workflow drafting, coding assistance, or analytics support when appropriate safeguards are in place.
Prohibited uses may include:
entering protected health information into unapproved AI tools
using AI outputs as a substitute for clinical judgment
relying on AI for diagnosis or treatment recommendations without authorized review
using unvalidated tools for patient-facing decisions
uploading confidential, proprietary, or regulated data into public tools
using AI-generated content without human review when accuracy matters
deploying vendor tools without governance review
The policy should be specific enough to guide behavior, but flexible enough to evolve as tools and use cases change.
Establish an AI Review and Approval Process
A healthcare AI policy should explain how new AI tools or use cases are reviewed before use.
This process may include:
an AI intake form
intended use review
risk tiering
privacy and security review
legal and compliance review
clinical or operational validation
bias and fairness assessment
vendor risk assessment
implementation planning
governance committee approval for higher-risk tools
The level of review should match the level of risk. A low-risk internal productivity tool should not require the same process as a clinical decision support model or patient-facing AI application.
The NIST AI Risk Management Framework emphasizes governance, mapping, measurement, and management as core functions for AI risk management. A healthcare AI policy can help translate those functions into organizational expectations.
Address Data Privacy and Security
Healthcare AI policy must be clear about data use.
Organizations should define what types of data may be used with AI tools and under what conditions. This is especially important for patient data, research data, employee data, confidential business information, and proprietary company materials.
The policy should address:
protected health information
de-identification requirements
secondary data use
data retention
vendor access to data
model training on organizational data
cybersecurity requirements
contractual protections
data storage and processing locations
If a tool uses patient or regulated data, the policy should require privacy, security, legal, and compliance review before use.
Require Human Oversight
AI outputs should not be treated as automatically correct.
A healthcare AI policy should define when and how human review is required. This is particularly important when AI may influence clinical care, patient communication, research decisions, trial recruitment, safety monitoring, or operational prioritization.
The policy should clarify:
who reviews AI outputs
what level of expertise is required
when AI outputs must be verified
when AI outputs cannot be used alone
how disagreements between human judgment and AI outputs are handled
how errors or concerns are reported
WHO guidance on AI for health emphasizes that humans should remain in control of healthcare systems and medical decisions.
Include Bias, Fairness, and Equity Requirements
AI policies should address bias and fairness directly.
Healthcare organizations should require AI tools to be evaluated for performance across relevant populations and settings when appropriate. This is especially important for tools that influence access, risk prediction, diagnosis, treatment, communication, enrollment, or resource allocation.
The policy should require teams to consider:
whether training and validation data are representative
whether subgroup performance has been assessed
whether outputs are actionable across populations
whether the tool could worsen disparities
whether monitoring includes equity-related metrics
whether community or patient perspectives are relevant to the use case
Bias mitigation should not be treated as a technical afterthought. It should be embedded into AI governance from the start.
Define Monitoring and Incident Response
AI policy should include expectations for post-deployment monitoring.
AI systems can change in performance over time. Patient populations shift. Workflows evolve. Vendor tools update. Model drift can occur. New risks may emerge after deployment.
The policy should define:
what performance metrics will be monitored
how often monitoring will occur
who reviews monitoring data
how subgroup performance will be assessed
what thresholds trigger escalation
how AI-related incidents are reported
when a tool should be modified, paused, or retired
FDA’s AI-enabled medical device resources also emphasize lifecycle considerations for AI-enabled software, including transparency, monitoring, and management of changes over time.
Train Staff on Responsible AI Use
A policy is only useful if teams understand it.
Healthcare organizations should train staff on approved AI uses, prohibited uses, data privacy expectations, human oversight, documentation, and escalation pathways.
Training should be practical and role-specific. Clinicians, researchers, operations teams, compliance staff, procurement teams, and executives may each need different guidance.
Training should also address shadow AI. Staff need to know when AI use is appropriate, when it requires approval, and where to go with questions.
What Leaders Should Do Now
Healthcare and life sciences leaders should ask:
Do we have a clear policy for AI use?
Does the policy cover generative AI and vendor tools?
Do staff know what data can and cannot be entered into AI tools?
Do we have an approval process for new AI use cases?
Do we define human oversight and accountability?
Do we monitor deployed AI tools after implementation?
If the answer to any of these questions is unclear, the organization likely needs a stronger AI policy.
Responsible AI Requires Clear Guardrails
AI policy is not about stopping innovation. It is about creating the conditions for responsible adoption.
A well-designed healthcare AI policy helps organizations reduce risk, support safer implementation, protect patient and organizational data, and give teams confidence about how AI can be used.
The organizations that lead in healthcare AI will not be those that allow AI use to grow without structure.
They will be the organizations that create clear guardrails, strong oversight, and accountable pathways for responsible AI adoption.
Need Support Developing a Healthcare AI Policy?
CROSS Global Research & Strategy advises healthcare, pharma, digital health, and life sciences organizations on responsible AI strategy, governance, validation, and implementation.
We help teams develop healthcare AI policies, define acceptable-use standards, assess vendor and use-case risk, and build oversight structures that support patient safety, equity, trust, and regulatory readiness.
To discuss how your organization can strengthen its healthcare AI policy and governance approach, contact CROSS Global Research & Strategy.
Suggested References
National Institute of Standards and Technology. Artificial Intelligence Risk Management Framework. National Institute of Standards and Technology; 2023.
World Health Organization. Ethics and Governance of Artificial Intelligence for Health: WHO Guidance. World Health Organization; 2021.
US Food and Drug Administration. Artificial Intelligence-Enabled Medical Devices. US Food and Drug Administration.
Coalition for Health AI. Responsible AI Guidance: Blueprint for Trustworthy AI. Coalition for Health AI; 2026.
URAC. Health Care AI: Accountability in Practice. URAC; 2026
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