AI in Clinical Trials: What Inspectors Will Ask

AI in Clinical Trials: Preparing for the Inspection Question

AI in clinical trials has moved past pilots and into everyday operations. Sponsors and contract research organisations use it for site feasibility, patient identification, protocol drafting, data quality checks, and risk-based monitoring. Regulators have noticed. Following joint principles on good AI practice in drug development published by the United States and European agencies early this year, the practical question has shifted from whether AI is permitted to how a sponsor will explain its use when an inspector asks. That conversation can now happen at trial authorisation and during routine inspections, not only at the marketing application.

Where AI Sits in a Trial and What Gets Questioned

Inspection risk tracks how close a use case sits to patient safety or the endpoint.

What the Joint Principles Actually Expect

The joint guiding principles published in January set out expectations rather than rules, and the themes are consistent with existing regulated practice. Define the context of use precisely, meaning what the model does, for which decision, with what consequences. Apply effort proportionate to risk. Keep humans central where decisions affect participants. Document data governance, model development, and performance assessment. Manage the system across its lifecycle rather than validating it once.

None of that is unfamiliar to a quality team. The discipline is the same one applied to computerised systems, which we covered in validating GenAI in pharma. What is new is scope creep: AI arrives through clinical operations, data management, and medical writing tools bought by different functions, often without the quality organisation being told.

How AI in Clinical Trials Arrives Without a Decision

Very few sponsors sit down and choose to adopt AI across a trial. It appears inside a recruitment platform, an electronic data capture module, a monitoring dashboard, and a medical writing assistant, each bought by a different function on a different cycle. The first time anyone assembles the full list is often when an inspector asks for it, which is exactly the wrong moment to discover that three vendors will not share documentation about how their models work.

Recruitment and the Representativeness Problem

Patient identification is the most enthusiastically adopted use case, because slow enrolment is the most expensive problem in clinical development. A model screening electronic health records can surface candidates faster than manual review, and that is genuinely valuable.

The risk is subtler than a wrong answer. If the screening model works better on patients with rich, well-coded records, it will systematically surface those patients, and populations with thinner records become less visible. That skews the trial population in a way that no single decision reveals, and it is hard to explain afterwards. The control is to measure who the model surfaced against who was eligible, not just how many candidates it produced.

Site selection deserves the same scrutiny. A feasibility model trained on historical enrolment will favour sites that performed well before, which usually means large centres in a handful of countries. That can be operationally sensible and still narrow the trial population, and it is the kind of pattern that surfaces as a regulatory question about generalisability rather than as an internal metric.

Protocol Drafting Sits in a Grey Zone

Current guidance concentrates on AI used for analysis, manufacturing, and endpoint-related decisions. Using a model to draft protocol text, statistical sections, or informed consent language sits less squarely inside that framework, which some teams read as permission and others as uncertainty.

The prudent position is that a protocol is a scientific document with regulatory consequences. If AI helped produce it, the author list, review record, and version history should make clear who examined the science, what was changed, and on what basis. Fabricated or misread citations are a known failure mode in drafting work, as seen repeatedly in other professions, so every reference belongs in the verification pass rather than the reading pass. Version control is the practical defence, because it shows the document as it changed rather than only as it ended.

Seven Questions to Answer Before an Inspection

If these answers exist in writing, an inspection conversation becomes routine.

1. Where is AI used across this trial? A current inventory covering vendor tools, not just systems built in house.
2. What is the context of use for each one? The decision it supports and the consequence if it is wrong.
3. Who reviewed the output, and when? Named, qualified reviewers with dated records.
4. How was performance assessed? Evidence proportionate to risk, including limitations.
5. Can data flows be traced end to end? From source record to analysis, including what the model touched.
6. What changes since validation? Model versions, prompts, and vendor updates, with the impact assessed.
7. How are participants protected? The specific controls where a model error could affect safety or eligibility.

Pros and Cons of AI in Trial Operations

The operational case is strong, which is exactly why the controls need to keep pace.

• Pro: faster enrolment. Screening records at scale shortens the slowest and most expensive phase of many trials.
• Pro: earlier quality signals. Cross-system checks surface inconsistencies while they can still be corrected.
• Pro: targeted monitoring. Oversight effort moves to the sites and data that actually need attention.
• Con: hidden population skew. Screening models can quietly favour patients with richer records.
• Con: tool sprawl. AI arrives through several vendors and functions, often outside quality oversight.
• Con: drafting errors that look authoritative. Generated text carries citations and numbers that read as verified.

Real Scenarios Worth Thinking Through

These scenarios are illustrative, showing how AI in clinical trials plays out in practice rather than presented as verified case studies.

A sponsor uses a vendor tool to rank sites by predicted enrolment. Enrolment improves, but at an inspection the team cannot explain which historical data the model used or whether it disadvantaged newer sites, because the vendor treats the model as proprietary and the contract never required disclosure.

A data management group deploys anomaly detection across forms and systems. It works well, and the number of queries drops. Nobody documented how flagged items are triaged, so the monitoring plan and the actual practice no longer match, which is the kind of gap inspections are designed to find.

A medical writing team drafts a clinical study report section with AI assistance. The narrative is accurate, but one cited reference does not support the statement attached to it. A reviewer catches it because the team added a reference verification step after seeing similar failures elsewhere.

“After testing multiple AI models on coding, research, and business prompts, combined outputs produced more reliable results than any single model.” Internal multi-model evaluation, Talkory research team.

Why Talkory Wins

Much of the judgement in trial operations comes down to interpreting text: eligibility criteria, protocol wording, and published evidence. Talkory puts the same question to GPT, Claude, Gemini, Grok, Perplexity Sonar, and Kimi K3 together, so a team can see whether an inclusion criterion reads the same way to six independently trained models. Agreement suggests the wording is unambiguous. Disagreement points to criteria that will be interpreted differently across sites, which is worth fixing before the protocol is final rather than explaining afterwards. The same approach catches citation and evidence problems in drafting work, a pattern we examined in AI hallucinations in scientific research.

Final Verdict

AI in clinical trials is now normal, and the expectations around it are becoming concrete. Keep a live inventory of where AI is used, define the context of use for each case, keep qualified humans accountable for anything touching participants or endpoints, and make data flows traceable from source to analysis. Treat vendor tools as part of your system, not someone else's. Sponsors who can answer the inspection questions in writing will keep the speed. Those who cannot will spend it on findings.