Which Uses of AI in Clinical Development Are Worth Testing Seriously?

Judging whether AI is worth trying requires a real task problem, checkable output and an understanding of what published studies evaluated. Evidence for a direction does not establish that any tool will work reliably in your team, but it can guide a more concrete pilot.

One candidate is retrieving and organizing public evidence.

Registry records, papers and existing documents are dispersed, creating repetitive search and extraction work. A bounded workflow can use fixed sources, return defined fields and preserve evidence locations for review. Measure value through actual review and rework, rather than generation speed alone.

Distinguish producing a reference-shaped string from retrieving an actual publication. A study of generated citations documented fabrication and errors in tested configurations. Understand whether a system retrieves sources or generates publication names from a model. Historical findings cannot be treated as the error rate of a current product.

Another candidate is criterion-level assistance with trial relevance.

The 2024 TrialGPT study in Nature Communications evaluated retrieval, matching and ranking, plus a limited clinician-assisted task. It supports testing this approach in a defined setting, but the evaluation has specific materials and boundaries. It does not establish identical benefit in every hospital or confirm final enrollment.

Such a workflow should provide evidence and uncertainty for each criterion, not only a matching score. Clinical staff need to assess missing records, changing health, additional protocol requirements and site availability. Access to patient data must fit the actual working environment. Public-material testing and real care cannot share all assumptions.

A third candidate is consistency and traceability checking in existing documents.

Test differences among a synopsis, protocol and schedule, or whether each endpoint has corresponding data collection. The ICH M11 common protocol structure provides an organizational basis without proving that a particular generation tool is compliant.

Known historical issues make useful evaluation cases. Give the system the same documents and check whether it finds genuine differences, creates false problems and lets reviewers locate the originals. Include poor scans, complex tables, long clauses and version changes rather than only clean excerpts.

Broad automation of development strategy needs separate claims and evidence.

Predicting study success, setting sample size, choosing sites and constructing external controls involve different models, data and assumptions. Each use needs appropriate validation. A coherent explanation is not evidence that all these tasks have been established as effective.

Record three evidence levels: what published research showed, what local testing observed and whether a business or operational outcome has been measured. They cannot replace one another. A publication supports a research finding; a local test supports task performance; less rework or better execution requires the team’s own records.

Set stopping conditions too: consequential errors cannot be detected consistently, source locations are missing, review is too costly or test materials poorly reflect real inputs. Ending an unsuitable pilot can free resources for more useful tasks.

Serious trials of AI can begin with defined materials and inspectable deliverables. Commit to one specific use, evaluate representative inputs and the complete review process, then decide whether to expand. That makes use of research progress while preserving accountability for the team’s own work.