AI Clinical Study Report Validation Checklist

Use this AI clinical study report validation checklist to prove that every important number, statement, change, and approval can be traced. The report is not ready because the prose sounds polished. It is ready only when qualified reviewers can reproduce the facts, explain the interpretation, and show who approved the final record.

This checklist is for clinical study report sections prepared with generative AI, retrieval tools, automated consistency checks, or other AI-assisted writing software. Match the depth of validation to the system's defined use and the risk of the report section.

1. Intended Use and Ownership

• The allowed AI task is written in plain language.
• The report sections covered by the validation are named.
• The study types, data types, users, and limits are defined.
• A study owner, system owner, and quality owner are named.
• Clinical, statistical, safety, medical writing, and regulatory review roles are assigned where needed.
• The team has defined what the AI must never decide or approve.

2. Approved Source Package

• The protocol and each approved amendment are identified by version.
• The statistical analysis plan and approved changes are identified.
• Final tables, listings, figures, and data definitions are included.
• Analysis populations and endpoint definitions are clear.
• Safety narratives, adjudication records, and deviation records are included when relevant.
• The approved report template and terminology list are current.
• The source package is locked, dated, and owned.
• A controlled process exists for replacing a source after drafting starts.

3. Data Protection and System Control

• The approved environment protects confidential study and participant information.
• Access follows job responsibility and is reviewed.
• Vendor retention, deletion, and secondary-use terms are documented.
• Study data are not used to train a shared model unless that use was reviewed and approved.
• The model, version, prompt or instruction set, template, and configuration are recorded.
• System changes trigger review before the next regulated use.
• Failures, downtime, and incidents can be reported and investigated.

4. Source Traceability

• Every factual claim links to an approved source.
• Every number links to the correct table, listing, figure, or approved record.
• Source links survive export into the review record or are preserved in a companion trace file.
• The system flags missing support instead of filling the gap.
• Conflicting sources are shown to the reviewer and not silently resolved.
• Quoted or closely paraphrased text is attributed when required.
• A reviewer can reproduce a sample of claims without help from the system vendor.

5. Numerical and Statistical Checks

• Participant counts match disposition and analysis population records.
• Endpoint values, confidence intervals, p-values, units, and rounding match approved outputs.
• Denominators are stated and consistent.
• Percentages can be reproduced from the cited source.
• Missing data and imputation methods are described correctly.
• Subgroup names and values match the approved analysis.
• Numbers remain consistent across the summary, body, tables, and conclusions.
• The text does not imply statistical or clinical meaning beyond the approved analysis.

6. Safety and Medical Review

• Adverse event terms and coding versions are correct.
• Serious events, deaths, discontinuations, and events of special interest match approved records.
• Exposure and observation periods are correct.
• Safety narratives do not omit facts that change clinical meaning.
• The report separates observed results from interpretation.
• Benefit-risk statements were written or approved by qualified people.
• Uncertainty and study limitations are clear.
• Safety reviewers can see the supporting records beside the draft.

7. Narrative Quality and Report Structure

• The section follows the approved CSR structure and study template.
• Terms, abbreviations, population names, and endpoint labels stay consistent.
• The prose states what the source supports without adding certainty.
• Important unfavorable, neutral, or missing findings are not softened or omitted.
• Methods, results, and interpretation remain distinct.
• Cross-references point to the correct section, table, listing, or figure.
• Repeated boilerplate still fits this study and has not carried over a wrong fact.
• The section can be understood without knowing how the AI system works.

8. Human Review and Change Records

• Generated text is marked as unapproved until review is complete.
• Reviewers have the source access and time needed to check the work.
• AI output, human edits, comments, and final text can be distinguished.
• Important corrections record the reason and reviewer.
• Rejected suggestions remain available when the controlled process requires them.
• Unresolved comments and conflicts block release.
• The audit trail can be exported and retained with the report record.

9. Performance Testing

• Tests use report sections and source formats that match the intended use.
• The test set includes ordinary cases, missing information, conflicting sources, and hard safety examples.
• Acceptance rules are defined before results are reviewed.
• Accuracy is measured by task, not reduced to one broad score.
• False support, invented facts, wrong numbers, and missed conflicts are counted separately.
• Review time and correction burden are measured.
• Performance is reassessed after a meaningful model, template, source, or workflow change.

10. Release Decision

• All required reviews and approvals are complete.
• Every release-blocking issue is closed.
• The final report version matches the approved record.
• Electronic signatures and approval records meet the organization's requirements.
• The final source manifest, system record, validation result, and audit trail are retained.
• The team knows how to correct the report if a problem is found later.

Do Not Release When

Do not release the report:

• when a source is missing
• a number cannot be reproduced
• safety text has not received qualified review
• a system change is unassessed
• reviewer comments remain open
• confidential data handling is unclear
• the audit record is incomplete

Those are control failures, not small writing issues.

How to Use the Checklist

Use the checklist before the pilot, during validation, and again before each release. Record pass, fail, not applicable, evidence, owner, and date for every item. A marked box without supporting evidence is not validation.

Related AI Medicine Now Guides

• Clinical Study Report AI Software
• Clinical Study Report AI Workflow
• Clinical AI Governance Framework
• AI Product Release Criteria for Clinical Tools
• FDA AI Inspection Readiness for Clinical AI Software

Reviewed: October 16, 2026. Next review: January 16, 2027.