Artificial intelligence (AI) is rapidly changing how pharmaceutical, biotech, medical device, clinical research, and regulatory organizations create and manage content. From drafting scientific materials and summarizing clinical evidence to developing digital campaigns and internal regulatory documents, AI can improve speed and efficiency. However, using AI in FDA-regulated communications introduces important compliance considerations. AI-generated content can contain inaccurate claims, omit important safety information, misrepresent scientific evidence, or create statements that are inconsistent with approved labeling. FDA’s regulatory expectations remain focused on the content and impact of a communication—not simply the technology used to create it. For prescription drug promotion, FDA expects communications to be truthful, non-misleading, appropriately balanced, and supported by relevant evidence. FDA also regulates false or misleading medical device labeling. Therefore, organizations should establish controlled processes before incorporating generative AI into FDA compliance, pharmaceutical marketing, medical device communications, or clinical trial communications.

1. Establish Clear Human Oversight for Every AI-Generated Communication

AI should support qualified professionals rather than replace regulatory, medical, legal, or quality judgment. A generated draft should never automatically become an approved communication simply because it appears scientifically credible or professionally written. Human reviewers should verify claims against approved product labeling, clinical evidence, established references, and the intended audience. This is particularly important for AI-generated content involving efficacy, safety, indications, limitations, contraindications, clinical outcomes, or comparative claims. FDA identifies overstated benefits, omitted risks, unsupported claims, misleading comparisons, and misrepresented study data as common concerns in prescription drug promotion. A documented review and approval workflow helps organizations demonstrate that appropriate subject-matter experts evaluated the final communication.

2. Keep AI Outputs Consistent With Approved Labeling and Evidence

Generative AI can produce convincing statements that are unsupported, outdated, or fabricated. For regulated organizations, every material claim should therefore be traceable to an authoritative source. Teams should verify AI-generated references, statistics, clinical trial results, product claims, and safety statements before publication.

For prescription drug promotion, FDA requires communications to be truthful and non-misleading, present effectiveness and risk information in a balanced manner, and disclose material facts. Similar principles are important when developing promotional labeling, FDA promotional communications, or materials concerning medical devices. Device labeling can be considered misleading when it contains false or misleading statements or fails to adequately communicate relevant information. Organizations should maintain approved source libraries and require reviewers to validate AI-generated claims against current versions of labeling, protocols, clinical evidence, and regulatory documents.

3. Protect Patient, Clinical, and Confidential Information

AI systems can create significant data privacy and confidentiality concerns when users enter patient information, unpublished clinical data, proprietary research, or regulatory submission content into external platforms. Before using an AI tool, organizations should understand how information is stored, processed, retained, and potentially used. For clinical research, this means establishing clear controls around protected health information, identifiable participant information, clinical databases, investigator materials, and confidential sponsor information. AI governance should also define which information may be entered into approved systems and which information must remain within controlled environments. A practical approach is to establish an internal AI-use policy covering approved tools, prohibited data, access controls, human review, documentation, and escalation procedures. These controls should align with existing quality, privacy, cybersecurity, and information-governance frameworks.

4. Validate AI Use According to Risk and Intended Purpose

Not every AI application carries the same regulatory risk. Using AI to correct grammar in an internal administrative document is fundamentally different from using AI to generate patient-facing safety information or content supporting a regulatory submission. FDA’s recent AI initiatives emphasize risk-based approaches. In January 2025, FDA issued draft guidance on using AI to support regulatory decision-making for drugs and biological products, including a framework for assessing the credibility of AI models according to their specific context of use. FDA has also developed AI-related recommendations for medical devices, including lifecycle management and documentation considerations for AI-enabled device software. Organizations should therefore define the intended use of AI, assess potential impact, establish validation criteria where appropriate, and document the controls applied. This risk-based AI governance approach can help distinguish low-risk productivity applications from high-risk regulatory or patient-facing uses.

5. Maintain Traceability, Documentation, and Continuous Monitoring

AI-generated communications should be managed within the organization’s existing quality management system and document-control processes whenever they affect regulated activities. Teams should be able to identify what AI tool was used, how the content was reviewed, what source material supported the claims, who approved the final version, and when the communication was released.

Continuous monitoring is also important because AI models, source information, product labeling, and regulatory expectations can change. FDA’s current AI work increasingly emphasizes lifecycle management, transparency, performance assessment, and clear essential information. FDA and international regulators have also identified principles such as human-centric design, risk-based approaches, data governance, multidisciplinary expertise, lifecycle management, and clear information as important elements of responsible AI practice in drug development.For medical device organizations, FDA’s August 2025 final guidance on predetermined change control plans for AI-enabled device software further demonstrates the agency’s focus on controlled, documented approaches to AI-related changes while maintaining reasonable assurance of safety and effectiveness.

Compliance Risks and Practical Industry Implications

Poorly controlled AI use can create regulatory risk, including inaccurate promotional claims, inconsistent labeling, inadequate risk disclosure, unsupported scientific statements, privacy concerns, and insufficient documentation. These problems can become especially serious when AI-generated content is published at scale across websites, social media, email campaigns, sales materials, or patient communications. FDA continues to monitor prescription drug promotion across different platforms and forms, with emphasis on truthful, balanced, and accurate communication. Consequently, organizations should treat AI as a productivity technology operating within existing regulatory controls—not as an exception to them.

For professionals responsible for regulatory affairs, medical affairs, promotional review, clinical research, quality, and compliance, the objective should be to create an AI workflow that combines efficiency with accountability. A strong process includes approved tools, defined use cases, qualified human review, evidence verification, privacy controls, version control, and documented approval.