EU MDR made clinical evaluation substantially more demanding than it was under the directives, and the practical effect for most manufacturers is a documentation burden that does not fit the resource they have. A clinical evaluation report has to be maintained rather than produced once, and post-market clinical follow-up feeds it continuously.
That shape — a long document, largely assembled from literature and post-market data, needing to stay current — is where AI assistance genuinely helps. It is also where one specific failure would be serious: a clinical claim the cited evidence does not support.
This is how we approach Clinical Evaluation Reports.
Why CERs became harder
Three changes compounded. The evidence expectations rose, with less tolerance for equivalence claims resting on a loosely similar device. The documentation has to be maintained across the lifecycle rather than assembled for a submission. And post-market clinical follow-up produces a continuous stream of data that has to be reflected back into the evaluation.
The result is that clinical evaluation moved from a project to a standing obligation, and most quality systems were built around the first model. The symptom is recognisable: a CER that was thorough when written and is now eighteen months behind the post-market data.
This is the same structural problem as validation debt in computerised systems. The document was accurate at a moment that has passed, and the gap is invisible until someone external looks.
Literature review is where the volume is
A systematic literature search, screening, appraisal and extraction is the bulk of the effort and the most mechanical part of it. Screening hundreds of abstracts against predefined inclusion and exclusion criteria is exactly the kind of high-volume, criteria-driven work that benefits from automation.
What has to be preserved is the auditability of the method. A literature review supporting a regulatory conclusion needs a documented search strategy, a reproducible record of what was screened, and a traceable reason for each exclusion. Automation that produces a conclusion without that record is not usable, however good the conclusion.
So the requirement is the same one that applies to every AI-assisted regulatory document: the output has to carry its provenance. clinical documentation is what that means in practice.
The failure that matters
A clinical claim that the cited source does not support. Fluent, plausible, and wrong — and in a clinical evaluation it is a claim about safety or performance, which is the most consequential category of error available.
This is not caught by an aggregate quality measure. A system can be accurate on average and still occasionally misattribute a finding or overstate a result, and the average tells you nothing about the instance. The evaluation of any AI used here needs a specific test for unsupported claims, separate from and in addition to general quality measurement.
The practical control is that every claim is checkable in place against the source it came from, so that review is verification rather than reading for plausibility. If a reviewer has to go and find the paper, verification degrades at exactly the point in a long document where attention is weakest.
What stays with the qualified evaluator
The clinical evaluation conclusion, the benefit-risk determination, the assessment of whether the evidence is sufficient, and any equivalence justification. These are judgements that a qualified clinical evaluator makes and signs, and they are the reason the role exists.
AI assistance belongs in the assembly: the search, the screening, the extraction, the descriptive synthesis, the consistency of the document against its sources, and keeping it current as post-market data arrives.
A system that drafts the conclusion is doing something categorically different from one that assembles the evidence, and it should be classified and validated on that basis rather than described in the same terms.
Keeping it current, which is the real win
The highest-value application here is not the first draft. It is maintenance.
Post-market clinical follow-up produces data continuously. New literature appears. Complaint and vigilance data accumulates. The question that matters each time is narrow: does this change the clinical evaluation, and if so which parts.
Where claims are linked to their sources, that question is answerable — you can identify which statements are affected by new evidence. Where the CER is a document maintained by hand, answering it means re-reading the whole thing, which is why it does not happen on schedule.
This is the difference between a document and a maintained model, and it is the same argument as The live traceability matrix in a different regulatory context.
Validating the system, and the AI in it
A tool producing content for a clinical evaluation is producing regulated records. It validates on the usual terms with the AI additions: a stated intended use including what it is not for, acceptance criteria set before evaluation, an evaluation set gating releases, a specific test for unsupported claims, a named human review role, drift monitoring, and a fallback.
Note that device manufacturers may also have EU AI Act obligations depending on the intended purpose of the AI, assessed separately from MDR. Satisfying one does not discharge the other, and a single tool can carry duties under both. AI/ML as Software as a Medical Device covers the related question of AI inside the device itself, which is a different problem from AI used to write about it.
TraceDraft is the product, Clinical Evaluation Reports the practice, and book a demo to see it against one of your own evaluations.
Where to go next
Explore GxP Copilot for AI-native validation, TraceDraft for source-traceable clinical documentation, or book a demo to see either on your own data.
