AI Governance in CME/CE

AI governance is the framework an organization uses to guide how artificial intelligence is selected, approved, implemented, monitored, and evaluated. It establishes clear accountability, policies, acceptable-use parameters, human oversight, data protections, disclosure practices, content-review standards, and escalation procedures. In CME/CE, effective governance helps organizations balance innovation with educational integrity, accreditation compliance, ethical responsibility, and learner trust.

ACCME and Joint Accreditation have issued formal guidance for the responsible use of AI in accredited continuing education. Their existing accreditation criteria and the Standards for Integrity and Independence continue to apply and guide the instructional design of accredited CME/CE.  Accredited providers remain responsible for ensuring that AI-assisted content is valid, evidence-based, balanced, independent of commercial influence, appropriately reviewed, and consistent with applicable disclosure and transparency expectations. Failure to meet these expectations could place an organization’s accreditation at immediate risk, reinforcing that AI governance is now a core compliance function for accredited CME/CE providers.  Read more here. 

AI may support educational planning, drafting, editing, research synthesis, content development, learner engagement, data analysis, and administrative workflows. However, the accredited provider retains full responsibility for the final educational content and cannot transfer or delegate that accountability. Existing accreditation criteria and the Standards for Integrity and Independence continue to apply and guide the instructional design of accredited CME/CE.

While it’s not currently mandated by accreditation boards, yes.  Disclosure of AI use is deemed a best practice. ACCME guidance states that disclosing AI use in educational content creation supports informed engagement and learner trust, particularly when AI is used to generate, modify, or analyze educational materials. A useful disclosure may identify the tool, version, date or period of use, purpose, extent of AI involvement, human-review process, and the individual or organization retaining responsibility for the final content.

AI-generated or AI-assisted content should undergo qualified human review before it is released to learners. The review should assess scientific validity, accuracy, currency, clinical reasoning, balance, potential bias, appropriate referencing, commercial influence, and alignment with the activity’s educational purpose. Organizations should also document who reviewed the material, what standards were applied, how identified concerns were resolved, and who approved the final content. ACCME specifically advises providers to maintain the same standards of content review and independence for AI-generated materials as for content developed by human authors.

Unless an organization has expressly approved the tool and confirmed appropriate legal, contractual, privacy, and security protections, users should not enter protected health information, personally identifiable information, confidential learner or faculty data, proprietary organizational information, unpublished research, passwords, financial information, client materials, or other restricted content. Organizations should define approved and prohibited inputs by tool, recognizing that an enterprise-licensed platform may provide different protections than a publicly available consumer system.

Begin by understanding how AI is already being used across the organization. Inventory current tools and use cases, assess governance and capability gaps, designate accountable leadership, and identify the areas of greatest risk. Organizations can then establish immediate guardrails, approve appropriate tools, define prohibited inputs, create human-review and disclosure expectations, and provide role-specific AI-literacy training. A phased roadmap can help sequence immediate actions, near-term policy development, and longer-term integration into organizational workflows.

An AI Governance Lead is the individual designated to coordinate and oversee an organization’s responsible AI efforts. The role may be assigned to an existing leader within the organization with decision-making authority. Typical responsibilities include coordinating policy development, maintaining the approved-tools inventory, clarifying accountability, supporting risk assessments, convening relevant stakeholders, overseeing training, and monitoring of AI use. The Governance Lead does not make every AI-related decision alone; instead, the role provides structure, coordination, and accountability across leadership, compliance, legal, IT, privacy, education, and operational teams, as may be applicable.