How AI Is Transforming CER Development for Medical Devices & IVDs

Clinical Evaluation Reports (CERs) have long been the foundation of regulatory submissions for medical devices and IVDs in the EU. As we move into 2026, the expectations around how CERs are researched, structured, and justified are evolving rapidly and are becoming increasingly rigorous. Manufacturers are finding that the old way of building CERs – stretching time, budget, and relying heavily on internal expertise is no longer feasible.

The conversation has shifted from “How do we write CERs faster?” to “How do we build stronger clinical evidence bases and more consistent documentation while maintaining quality?”
This is where AI-enabled workflows are beginning to change the dynamic.

Rather than serving as a shortcut, AI is reshaping how teams approach the development of CERs- how clinical evidence is identified, contextualized, and mapped to regulatory requirements.

1. What’s Changing in CER Development for 2026

Greater emphasis on methodological transparency

Notified Bodies are now looking beyond the final report and into the logic behind every methodological decision. Search parameters, article selection pathways, appraisal frameworks, and evidence grading systems must be clearly justified and consistently applied.

Annual clinical evidence updates are no longer flexible

For many years, update cycles varied widely, and the determination of when an update was due was often inconsistent—whether aligned to the end date of the data range evaluated, the formal release date of the document, or the date the evaluation process was initiated. However, with more structured and prescriptive Notified Body review processes, this ambiguity is being reduced. But with more structured Notified Body review processes, annual updates—especially for Class IIb/III and high-risk IVDs—are being enforced with far more uniformity.

Deeper integration of SoTA, PMS, PMCF, and risk files

Regulators expect a tight relationship between:

• State of the Art claims
• Available clinical data
• Residual risks
• Post-market findings
• PMCF commitments

Any disconnect between these components is now seen as an indicator of weak evidence strategy.
These shifts mean manufacturers must redesign how CERs are built, not just how they are updated.

2. Where Manufacturers Are Struggling Today

1. Traditional SLR processes no longer match regulatory speed

   Manually conducting and synthesizing a Systematic Literature Review (SLR) for each update cycle is increasingly unsustainable. Teams lose time on screening, categorization, and repetitive extraction tasks, significantly delaying timelines.

2. Lack of portfolio-wide visibility slows down progress

   CERs are often developed in silos. Without shared data, evidence repositories,
   and aligned templates:

   • Writers redo work
   • Literature overlap is missed
   • Review cycles expand
   • Cross-device consistency becomes difficult

   CER development needs a foundation that supports scale, accuracy, and collaboration.

3. How AI Strengthens CER Development from Start to Finish

AI doesn’t take the place of clinical judgment. Instead, it creates a structured backbone for CER development – letting teams focus on higher-value analysis rather than time-consuming manual work.

AI accelerates literature evaluation

AI tools can rapidly scan and sort hundreds of articles while extracting:

• Outcomes
• Study design details
• Safety findings
• Device-specific information

This allows writers to begin synthesis much earlier in the process.

Evidence is automatically grouped for better traceability

AI-enabled categorization helps organize:

• Safety evidence
• Performance outcomes
• Material-level considerations
• Clinical background
• Adverse events

This simplifies synthesis and supports a clear evidence narrative.

Content structuring supports faster drafting

AI-supported drafting tools create:

• Pre-populated sections
• Literature summaries
• PMS and PMCF extracts
• Risk-linked narratives

Writers spend more time refining clinical arguments and less time drafting repetitive content.

4. Manufacturer Priorities for 2026 CER Development

Standardized templates for consistent drafting : Templates aligned with regulatory expectations help teams minimize variability – essential when multiple writers contribute to large portfolios.

Structured data repositories for clinical evidence : Unified repositories prevent the duplication of literature screening and create a single source of truth for drafts and updates.

Shorter review cycles enabled by traceability : Reviewers move faster when evidence links are clear and each statement can be easily traced back to its source.

5. Practical Steps to Strengthen CER Development

Create centralized repositories for evidence and historical CER data : This prevents teams from starting from scratch – especially for updates.

Build reviewer-friendly audit trails : Linking every data point to a source reduces back-and-forth communication and supports efficient internal sign-off.

Develop an annual roadmap for clinical evidence reviews : Instead of reactive drafting, plan literature cycles, internal reviews, and template updates across the year.

How Celegence Can Support AI-Enabled CER Development

Celegence offers AI-supported medical writing, clinical evaluation, and data structuring services designed specifically for MDR and IVDR compliance. Using CAPTIS®, our AI-powered authoring platform – we help manufacturers strengthen CER development, streamline evidence generation, and maintain consistent quality across device families.
To learn how we can support your CER strategy for 2026, contact us at info@celegence.com.