The useful question about AI in statistical programming is not whether it can produce plausible code. It is whether a team can establish an appropriate role for that code, detect failure, and retain responsibility for the resulting evidence. A fluent explanation should never become the acceptance criterion for a clinical analysis.
The FDA’s January 2025 AI guidance remains a draft with nonbinding recommendations. It proposes assessing credibility in a defined context of use. It is not a general approval of AI systems or evidence that a particular product is suitable for regulatory work.
1. Specify what assistance means
Separate drafting a program, suggesting a mapping, explaining an existing output, and generating evidence intended to support a regulatory decision. These activities have different risks. Define the inputs the assistant may access, the outputs it may produce, and the decisions reserved for qualified staff.
A hypothetical assistant that drafts a descriptive laboratory plot should not quietly determine the primary analysis population. Write down the boundary before evaluating performance. The statistical plan and approved specifications remain the authority for those decisions.
2. Evaluate difficult cases, not demonstrations
A demonstration usually shows a well-formed request and a successful answer. Evaluation should also include ambiguous endpoint wording, inconsistent units, duplicate records, missing visits, and prompts that ask for an unsupported causal conclusion. Test whether the system exposes uncertainty or silently invents a resolution.
The NIST Generative AI Profile offers a broader risk-management reference. Our proposed test design includes both code correctness and behavior: does the assistant identify missing information, avoid executing unauthorized changes, and preserve the distinction between exploratory and approved work?
3. Review executable evidence
Inspect the program, input versions, dependencies, logs, warnings, and result. Compare selected calculations to independently established expectations. A generated narrative about the program cannot substitute for inspecting what was actually executed.
Keep the model version and prompt context where they are relevant to reproducibility, while protecting sensitive information. Decide how a model update triggers reassessment. A previously accepted evaluation does not automatically establish equivalent performance after a change.
4. Keep approval visible
The output should communicate whether it is a draft, exploratory result, reviewed analysis, or released deliverable. Design the workflow so that copying a chart does not strip away that status. Identify the human reviewer and the scope of their approval.
ICH E6(R3) provides the clinical-trial oversight context. The practical goal is accountable assistance: a team can explain the purpose of automation, demonstrate its controls, and identify who approved the evidence. Faster drafting is useful only when the review contract remains clear.
