Speeding Systematic Literature Reviews with AI for CER Compliance

Systematic literature reviews are a central component of Clinical Evaluation Reports. However, searching multiple databases, screening thousands of titles, extracting structured data and preparing audit-ready documentation often absorbs the author’s time and delays progress. When used responsibly, AI can accelerate these repetitive tasks while preserving reproducibility and traceability.

Common Bottlenecks in Traditional SLRs

Teams frequently encounter the same constraints:

• Running inconsistent and inadequate queries across databases and managing large duplicate sets.
• Manual title and abstract screening that consumes significant SME/author hours.
• Extracting study-level fields such as design, population, endpoints and harms into a consistent dataset.
• Producing PRISMA-style documentation, version control and traceability for audits and Notified Body review.

These efforts are essential for MDR compliance but can also significantly impact timelines and quality.

What AI Contributes, with Safeguards

AI is most valuable for repetitive, pattern-based work. Key capabilities that benefit SLRs include:

• Automated retrieval and de-duplication across multiple sources.
• Assisted screening that ranks records by relevance, keeping reviewers in control.
• Rapid structured extraction into a standardized data dictionary.
• Automatic generation of PRISMA flows and exportable audit trails.

AI should extend human capability, not replace it. Every AI generated result must link back to the source and remain verifiable by reviewers.

A Regulator-Ready Hybrid Workflow

Below is a practical sequence that balances automation with mandatory human checks:

• Scope and protocol: Define the clinical questions, populations, comparators and acceptance criteria; pre-register search strings, databases and date ranges.
• Automated retrieval and clean-up: Run pre-registered searches, remove duplicates and tag records for screening.
• Assisted screening with adjudication: Use AI scoring to assess records; two reviewers confirm inclusions and resolve disagreements.
• Full-text extraction and verification: AI extracts data based on pre-defined instructions into templates; reviewers verify critical fields such as endpoints and harms.
• Quality appraisal and synthesis: Appraise study quality, apply pre-defined criteria for benefit-risk, and conclude study findings accordingly.
• PRISMA output, traceability and version control: Produce a PRISMA flow diagram, an exportable study table and a traceability matrix linking claims to evidence and risk controls.

This approach reduces the human oversight regulators expect while automating the steps that typically consume the most time.

Mini Case Example

Consider a typical SLR for a Class IIa device that searches five databases and returns 3,500 records. In a manual workflow, title/abstract screening and full-text extraction might require four to six weeks of an SME’s time. With automated retrieval, de-duplication and assisted screening, the initial screening pool can be reduced by more than half within days. Assisted extraction then populates a structured dataset that SMEs can validate in a matter of days rather than weeks. Deliverables such as the PRISMA flow and an exportable study table. The net effect is savings of several weeks in SME time, faster responses to Notified Body queries and a cleaner audit record that documents reviewer verification of AI-extracted items.

Integrating Real-World and Decentralized Data

Regulatory reviewers increasingly expect real-world evidence, registries and decentralized trial outputs to support CER updates. AI can help harmonize heterogeneous inputs by tagging metadata, tracking sources and converting registry exports into structured records for appraisal. The same human-in-the-loop principles apply here, and the analytical validation and reviewer verification are required before RWE is used in benefit-risk conclusions.

Operational Benefits

Adopting this hybrid model delivers measurable improvements:

• Shorter cycles because searches, de-duplication and extraction are automated.
• Fewer data-entry inconsistencies due to structured extraction.
• Better audit readiness through consistent PRISMA outputs and per-item provenance.

These gains free the author and the subject experts to focus on interpretation and clinical judgment rather than repetitive data handling.

Governance and Team Readiness

Introducing AI in your workflow should include clear guidelines. Define standard operating procedures specifying where AI is permitted, which fields require mandatory human verification, and how AI determined scores should be interpreted. Train reviewers to identify common AI errors and record corrections. Periodic audits comparing AI outputs with manual benchmarks help maintain quality and build trust with internal stakeholders and external reviewers.

Below is a quick SLR Checklist when automation is used

• Pre-specify search strategy and inclusion/exclusion criteria in a protocol.
• Utilize exportable, audit-ready outputs including PRISMA flows and data dictionaries.
• Keep reviewers in the loop for screening and extraction verification.
• Maintain a traceability matrix linking claims, evidence and post-market outputs.
• Archive logs showing search timestamps, reviewer decisions and data sources.

How CAPTIS^® Can Help

Integrated platforms that combine literature search, collaborative review, structured extraction and automated reporting make this hybrid workflow practical on scale. CAPTIS^® automates extraction, generates audit-ready reports and preserves references while enabling simultaneous review and simple cross-verification. Features that accelerate compliance include an integrated citation manager that links each extraction to source documents, exportable audit logs for every reviewer decision, role-based review workflows and pre-built templates for traceability matrices. That mix helps teams shorten SLR cycles without sacrificing traceability or regulatory rigor. Read How AI Speeds Up Systematic Literature Reviews by 60% blog here.

Conclusion

A rigorous protocol, clear reviewer responsibilities and tools that produce verifiable outputs make it possible to utilize automation and reduce SLR timelines while improving consistency and auditability. Teams that adopt a balanced AI-plus-reviewer approach will be better positioned to meet Notified Body expectations and keep CERs current with high-quality evidence. By leveraging AI-powered tools like CAPTIS^®, medical device professionals can ensure compliance with regulatory requirements while saving time and resources. Stay ahead of the curve by embracing AI in your SLR process. See how our AI-powered solutions can transform your regulatory processes. Contact us at info@celegence.com to learn more.