For MedTech organizations, clinical evaluation is not just a documentation exercise. It is central to product safety, regulatory confidence, market access and patient outcomes.

But as product portfolios expanded and evidence requirements became more demanding, a global MedTech leader faced a growing capacity challenge. Its clinical specialists were being pulled deeper into manual, repetitive work, including screening literature, extracting evidence, preparing documentation and updating Clinical Evaluation Reports (CER), at the same time that regulatory expectations were becoming more rigorous.

The business needed to move faster, but not at the expense of scientific quality, traceability or expert oversight.

We helped the organization reimagine clinical evaluation as a more scalable, AI-enabled operating model, one where intelligent agents handled evidence-intensive workflows and clinical experts could spend more time applying judgment, assessing risk and supporting better patient outcomes.

Clinical evaluation sits at the intersection of science, regulation and patient safety. Every evidence source must be reviewed carefully. Every conclusion must be traceable. Every document must stand up to regulatory scrutiny.

That level of rigor is essential, but the existing process was becoming increasingly difficult to scale.

The organization was managing growing volumes of evidence, evolving EU MDR expectations, fragmented workflows and recurring documentation updates. Highly specialized experts were spending significant time on literature screening, evidence extraction, summarization and document preparation, tasks that were necessary, but not the highest use of their expertise.

The business challenge was clear: how do you accelerate clinical evaluation while maintaining the trust, quality and oversight required in a regulated MedTech environment?

We implemented AIRA - AI-powered Intelligent Regulatory Automation to help orchestrate the clinical evidence lifecycle through a network of specialized AI agents.

Unlike traditional automation, which typically follows predefined rules for isolated tasks, AIRA was designed to support a connected workflow. Dedicated agents worked together to screen literature, extract relevant evidence, summarize findings, generate evidence-linked content, validate outputs against source material and route exceptions to clinical experts for review.

The model did not remove human judgment from the process. It made that judgment more scalable.

Clinical specialists remained accountable for interpretation, risk assessment and regulatory decisions. The AI agents handled repetitive, evidence-heavy work around them, creating a faster, more consistent and more traceable foundation for clinical evaluation.

This helped the organization move from manual throughput to a more intelligent operating model: one where people and AI worked together, with governance built into the process.

By automating evidence-intensive activities and reducing workflow inefficiencies, the client increased clinical writing productivity by 50%.

Instead of scaling operations through additional resources, the organization unlocked substantially greater capacity from existing teams, creating a more sustainable model for supporting growing product portfolios and regulatory requirements.

Faster regulatory readiness and submission support

Literature review turnaround times improved by 40%, while evidence identification and extraction activities were completed 60-90% faster than previous manual processes.

This enabled clinical teams to move quickly from evidence collection to evaluation, reducing bottlenecks and accelerating readiness for regulatory activities.

35% lower effort for recurring CER updates

By creating a continuously organized and traceable evidence ecosystem, AIRA reduced the effort required for recurring CER updates by 35%.

Rather than repeatedly searching for and processing information, specialists could focus on evaluating evidence and determining its clinical significance.

Greater consistency, quality and compliance at scale

Agentic AI helped standardize evidence assessment, summarization and content generation, resulting in:

By integrating quality, traceability and validation directly into the workflow, the organization strengthened confidence in regulatory documentation while reducing variability and rework.

AIRA provides a reusable foundation for expanding AI-enabled automation across CER authoring, periodic updates, post-market surveillance, regulatory submissions and other evidence-intensive workflows.

The next phase will focus on increasing content reuse, strengthening validation, integrating additional evidence sources and scaling governed workflows across accounts and business units. Human oversight, transparent evidence links and continuous quality measurement will remain central, enabling MedTech organizations to accelerate clinical documentation without compromising scientific integrity, compliance or trust.