Which Clinical Development Tasks Should You Delegate to AI First?

For a first AI task in clinical development, prioritize whether the team can readily check the result. Defined source material, a defined output and an accountable reviewer make it easier to move from a demonstration to routine use.

Separate organizing existing information, proposing options for discussion and making decisions that affect research or care. These need different checks. Converting eligibility criteria into a comparison table is a different task from deciding to relax one of those criteria.

A useful starting point is bounded evidence organization.

Given five registry records, for example, extract populations, comparators, outcomes and major restrictions. Or extract results from specified papers with their table locations. Keep the original wording or location, source version and uncertainties. AI produces an inspectable table; the responsible person checks the critical cells.

Another candidate is consistency checking in existing documents: differences between a synopsis and assessment schedule, or an abbreviation used inconsistently across sections. Distinguish mechanical checks from professional conclusions. Matching dates can be verified directly; judging whether a schedule is scientifically appropriate requires clinical, statistical and operational expertise.

Define acceptance before starting.

A comparison table can require a source for every factual entry and “not described in public material” for missing information. A protocol check can require both relevant locations and an explanation of the suspected inconsistency. Do not reward filling gaps with invented facts. References that appear complete also need verification. An original Scientific Reports study documented fabricated references and citation errors in particular tested model configurations. That supports a separate citation check, not a claim about the error rate of every current model.

To judge value, record drafting, review and rework time together. Generation speed alone misses the work needed to resolve mistakes. Track omitted restrictions, failed source locations and unsupported inferences. Analyze errors that change the population or conclusion separately rather than averaging everything into one score.

Consider an internal pilot using a fixed set of materials to compare the final deliverables of the current workflow and an AI-assisted workflow. Define what must be correct before testing, then hold sources and output format constant. This is a proposed pilot design, not a claim that this team has measured time savings. Include long clauses, exclusions, exceptions and version differences, rather than only easy examples.

Redefine the scope when AI supports formal evidence or decisions.

The FDA’s January 2025 draft guidance on AI for drug regulatory decisions, still listed as draft when checked, proposes evaluating credibility for a specified context and risk. An internal search assistant and a model generating decision-critical regulatory data need different assessments despite sharing the AI label.

For each task, write a short scope statement: inputs, outputs, reviewer, decisions it may support and handling of missing information. Put it at the workflow entry point. This can make boundaries more durable than a long prompt alone.

Begin with a genuine need, traceable sources and checkable results. Expand once review effort is actually reduced and errors are understood. Consistently producing a reliable evidence table is a useful foundation for the next task.