AI can generate code, suggest statistical approaches and help structure analysis documents.
But producing code is not the same as producing reliable statistical evidence.
In regulated work, results must remain understandable, reproducible, reviewed and traceable.
Statistical Analysis Plans
AI can support SAP structure, first drafts of standard sections, consistency checks, alignment with protocol endpoints, wording of analysis methods and identification of missing definitions.
The statistical reasoning and final decisions must remain with the statistician.
TLF Planning
AI can help prepare TLF shells, programming specifications, population definitions, proposed display structures, standard footnotes and consistency checks between SAP and outputs.
Programming Support
AI can assist with R or Python code generation, syntax conversion, code explanation, debugging, documentation, repetitive programming tasks and draft test cases.
Generated code must still be reviewed, tested and validated for its intended use.
Quality Control
AI can support QC by suggesting test cases, identifying inconsistencies, comparing specifications and outputs, highlighting suspicious results, checking naming conventions, preparing review checklists and explaining differences between two implementations.
Reproducibility and Traceability
Every AI-supported result needs documented inputs, version-controlled code, clear specifications, review records, reproducible execution, appropriate testing and defined responsibility.
Interpretation
AI may help summarise results, but it can miss study context, overstate conclusions or produce convincing but incorrect explanations.
Interpretation therefore remains a professional responsibility.
Conclusion
AI can accelerate programming and documentation, but reliability depends on review, testing and traceability.