How GenAI is already transforming regulatory writing for the life sciences
From EU Medical Device Regulation (MDR)-compliant clinical evaluation reports (CER) for medical device and diagnostic manufacturers to chemical, manufacturing, and controls (CMC) sections for pharmaceutical manufacturers, precision matters when providing regulatory agencies with comprehensive information. In both domains, regulatory teams continue to face painstaking manual processes, increasing document volumes, and rising demands for speed and quality.The pain points below make regulatory writing an area ripe for disruption by generative artificial intelligence (GenAI):
Manual, time-intensive authoring
Drafting and maintaining large regulatory documents such as CMC sections or CERs demands an enormous investment of time. Regulatory writers often spend weeks manually extracting and aligning technical data from disparate sources, including stability protocols, clinical studies, safety reports, and technical files.
Inconsistent source data
Documents from contract manufacturing organizations (CMOs), clinical sites, and internal research and development (R&D) teams are rarely standardized. Writers grapple with scanned PDFs, tables embedded in poorly formatted documents, missing metadata, or inconsistent certificate of analysis (CoA) formats—all of which slow down authoring and introduce risk.
Regulatory risk and compliance burden
As global authorities, such as the US Food and Drug Administration (FDA), European Medicines Agency (EMA), and notified bodies tighten expectations for lifecycle maintenance and documentation consistency, teams face mounting pressure to update dossiers quickly while maintaining quality. Manual workflows often mean missed details, inconsistent terminology, and extended review cycles.
Data quality and traceability challenges
With multiple stakeholders involved in data generation, ensuring that regulatory submissions maintain strict traceability to source documents becomes increasingly tricky. Errors or inconsistencies identified during a health authority review can result in costly delays.
Case study: AI pilot results signal possibilities in technical and medical writing
The concrete outcomes from recent pilot studies demonstrate that AI-driven regulatory writing is practically possible today, and pharmaceutical manufacturers should stay aware of advancements in this space.
In a 6-month AI-focused pilot, we used proprietary technology to support the generation of critical CMC documents for submissions to the US Food and Drug Administration (FDA) and the UK Medicines and Healthcare products Regulatory Agency (MHRA). The manufacturer’s team provided source materials ranging from manufacturing protocols to analytical method validations alongside corresponding approved submissions. Our regulatory and engineering team utilized various automation techniques, including large language model (LLM) content extraction and generation, to produce initial drafts of sections such as drug substance descriptions, stability summaries, and specifications.
The results were striking:
- 80%–95% alignment with previously approved submissions: The AI-generated drafts closely mirrored previously approved documents with minimal manual intervention.
- 60% faster first-draft turnaround: What previously took weeks was completed in minutes, freeing regulatory teams to focus on review and strategic activities.
- Improved consistency and accuracy: Tables, figure legends, and technical terminology were standardized across sections, reducing common formatting errors that plague submissions.
- Streamlined data integration: The AI tool seamlessly handled fragmented and variably structured input files, extracting usable information from scanned documents and handwritten annotations.
The manufacturer’s team noted that the AI drafts were indistinguishable from their own and that AI-generated tables were cleaner and easier to follow.
In a separate pilot in the medical device domain, AI-assisted drafting demonstrated positive results for specific sections of MDR-compliant CERs, which require substantial time and resources. Internal expert writers already leverage literature review automation and GenAI-driven content generation, thereby accelerating evidence synthesis, all while maintaining strict linkage to source data via retrieval-augmented generation (RAG) techniques.
Why regulatory teams are prioritizing AI now
Regulatory and medical writing teams are no longer asking if they should adopt AI, but how fast can they move to implement it?
Specifically, the benefits mentioned below are already being realized by the industry:
- Accelerated literature reviews: Literature review tools that integrate with databases like PubMed, Europe PMC, and Google Scholar to automate the screening and extraction process. Writers can complete systematic literature reviews for CERs, generate clinical summaries with AI, and fulfill other requirements significantly faster, with clear traceability to each source.
- Consistency across global submissions:
Whether drafting a stability protocol update, a CER clinical data analysis, or a CMC batch record, AI frameworks standardize formatting, terminology, and structure to align with ICH, MDR, and FDA guidelines, reducing regulatory queries and review cycles. - Reduced time and costs:
With writing speeds up to 50% faster and fewer revisions required, regulatory teams can meet aggressive submission timelines while controlling operational costs. This is especially critical as companies face increased submission volumes across global markets. - Improved data quality and reviewability:
By embedding AI into the drafting stage, errors from manual data transcription and copy-paste inconsistencies are drastically reduced. RAG provides answers based on source documents and can provide source links for answers or to help identify ambiguous data early, preventing delays during final regulatory review. - Support for lifecycle management: AI systems can’t just be suited for initial submissions. As products evolve, lifecycle updates—such as postmarket surveillance (PMS) reports, postmarket clinical follow-up (PMCF) plans, or CMC variations—can be generated and updated more quickly, maintaining audit readiness. Accurately detecting changes in source documents will be a key part of this element.
The Bottom Line: Regulatory AI is here to stay, but it requires time, attention, and partnerships to implement correctly. AI in regulatory and medical writing is no longer solely aspirational; it’s real and usable today. Our recent pilot studies team demonstrated that AI can deliver transformative results when paired with expert human oversight. This results in faster drafting, better consistency, stronger compliance, and cost efficiencies, freeing regulatory teams to focus on strategic value and doing way more with less.
The most important activities manufacturers can start to look at when preparing for AI and automation are:
- Map your source data to documents: Ensure data is high quality, structured, and machine-readable (when possible)
- Reduce noise and increase granularity: Cleaner, more specific data improves AI accuracy and output quality
- Be aware of nuances in your process: If a human struggles to interpret a document, AI will too. How much of your writing process is repeatable, vs. individual nuance?
About Celegence:
Celegence provides regulatory services and technology solutions tailored to the life sciences industry. Their AI-driven platform, CAPTIS®, and solutions like Dosscriber™ and Dossplorer™, help pharmaceutical and medical device companies accelerate documentation, improve compliance, and reduce the cost and burden of regulatory submissions globally.
Punya Abbhi is chief operating officer at Celegence.
