In the medical device sector, regulatory documentation is the gateway to market access and ultimately to patients. Yet, preparing and maintaining submission dossiers, clinical evaluation reports, and post-market updates is notoriously time-consuming and resource-intensive.

With the rise of Large Language Models (LLMs) and generative AI, a new opportunity emerges: using these tools as intelligent assistants for drafting, reviewing, and harmonizing regulatory documents. The goal is not to replace human expertise but to amplify it—accelerating workflows while maintaining compliance and precision.

Why Regulatory Documentation Is a Bottleneck

The challenges of medical device submissions are well known:

• Expanding requirements: European MDR and IVDR have significantly increased demands for clinical evidence, risk management, and post-market surveillance.
• Complex technical language: Documents must follow standardized structures (STED, CTD) and use precise, consistent terminology.
• Frequent updates: Change control, vigilance, and market feedback trigger ongoing revisions.

The result is long timelines, high costs, and risk of inconsistencies that may delay approval.

How LLMs Can Support Regulatory Workflows

Drafting initial dossiers

LLMs can generate structured drafts aligned with regulatory templates, for example:

• Producing introductory sections of the Clinical Evaluation Report (CER).
• Drafting consistent product descriptions across documents.
• Harmonizing terminology throughout submissions.

Reviewing and ensuring consistency

• Detecting terminology inconsistencies between dossier sections.
• Improving readability and style without compromising technical accuracy.
• Supporting multilingual coherence when paired with specialized translation tools.

Supporting change control

When specifications or modules are updated, AI can highlight which dossier sections require revision—minimizing omissions.

Limitations and Cautions: AI Is Not a Substitute

While promising, generative AI must be applied carefully:

• Accuracy: LLMs do not guarantee regulatory correctness; outputs must always be validated by experts.
• Bias and incompleteness: A well-written draft may contain subtle but critical errors.
• Accountability: Regulatory authorities (EMA, FDA) do not recognize AI as an author; responsibility remains human.

In short, AI is an accelerator, not a decision-maker.

Emerging Use Cases in the Field

• Regulators exploring AI: EMA and FDA have started assessing AI for clinical data review and submission simplification (FDA Digital Health Center of Excellence, EMA AI Policy 2024).
• Company-specific LLMs: Some med-tech firms train internal LLMs on past dossiers and guidance documents to automate drafting.
• RegTech startups: A new wave of companies is developing AI-powered compliance platforms tailored to medical devices.

Strategic Benefits for Med-Tech Companies

Organizations adopting generative AI in regulatory workflows can expect:

• Up to 30–40% faster dossier preparation, based on early pilots.
• Better allocation of human expertise to high-value tasks: strategy, risk analysis, authority interaction.
• Increased perceived quality thanks to greater consistency and standardization.

Conclusion: A Human–AI Partnership

The future of regulatory documentation will not be written by machines alone but by human experts empowered by AI assistants. Generative AI offers a way to reduce complexity, accelerate compliance, and support faster time-to-market.

For med-tech companies, the message is clear: those who embrace AI responsibly will gain a competitive edge in navigating today’s regulatory landscape.

👉 Interested in exploring how AI can streamline your regulatory pathway? Contact Creanova to discuss how we support medical device design, development, and documentation.