How Should You Professionally Review an AI-Written Study Synopsis?

A synopsis helps a team decide why a study should be done, what it will answer and whether the design deserves further development. AI can draft structured material quickly; professional review should assess design choices before polishing wording and format.

Ask for the input basis.

Clinical context, existing data, intended population, constraints and open decisions should be visible. Classify numbers, doses, dates and thresholds as evidence-based inputs, team instructions or generated suggestions. Unsupported suggestions belong in discussion material rather than being presented as approved parameters.

Start with one sentence: “In which population will this study compare which arrangements, using which outcome to support which next decision?” Different answers across functions indicate an unresolved question. Writing a full protocol may simply spread that disagreement across more sections.

Review plausible alternatives.

A model’s choice of a single arm, randomization, open-label design or comparator needs a rationale. Discuss at least one relevant alternative and the differences in evidence, ethics, timing and execution. No design dominates every dimension; make the trade-off explicit.

Read the primary outcome together with the decision standard.

Can it answer the question? Is the observation period adequate? What data does assessment require? What findings would change the next step? A team can discuss advance criteria for an exploratory study, but internal criteria are not identical to regulatory approval requirements.

Review sample size separately. Statistical staff should check effect, variability or event assumptions, error control, attrition and related provisions. A precise model-generated number does not establish its basis. If key inputs are missing, identify them and compare reasonable scenarios rather than carrying forward an unexplained number.

Connect eligibility with execution.

Who can provide the records? How long does testing take? Is treatment history available? Can sites interpret the population consistently? Operations review can identify screening bottlenecks. Any criterion change still needs scientific and safety justification rather than simply a larger potential pool.

Bring safety and stopping considerations into early review.

Do monitoring, dose adjustment and stopping proposals fit the available evidence? Assign owners to missing clinical or pharmaceutical information. A fluent synopsis should not imply that unresolved procedures are already determined.

The full protocol will require further structure. The ICH M11 guideline and template provide a common basis. Before expansion, retain a design decision table: question, chosen option, alternatives, basis, open issues and owner. Subsequent writing can trace decisions rather than merely lengthen sentences.

Possible internal outcomes are “design direction can proceed,” “additional evidence or revision needed before reconsideration” and “insufficient basis to proceed.” These are suggested discussion states, not universal regulatory categories. Each needs a defined action and conditions for the next review.

For illustration, an AI draft proposes a broader population while early evidence covers only a narrower group. Ask what safety and effect information would support expansion and assign the assessment. Do not accept expansion because it already appears in the draft. This is a teaching situation, not an actual asset review.

End with three answers: which design choices are confirmed, which remain assumptions and who will resolve each gap. A full protocol can then develop on a clearer basis, reducing avoidable drafting and design rework.