CRO.01
Trial Protocol Drafting
First-draft protocols to the ICH M11 template from your synopsis and prior protocols, checked for consistency and eligibility realism, with the sponsor signing every section.
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Clinical trial AI,
inspection ready.
The documents move faster. People still sign. Protocol drafting, site feasibility and trial master file QC for sponsors and CROs. Built so every output can be traced, every action is logged and the accountable person under GCP stays accountable.
Last reviewed 30 October 2026 · Engineering guidance, not legal or regulatory advice.
In context
AI in clinical trials is most useful on the documents around the science: drafting protocols to a structured template, ranking sites on real evidence and checking the trial master file before an inspector does. It speeds the work; investigators, sponsors and CRO staff still make and sign every decision.
Where it breaks today
01
Protocols drive amendments
Eligibility criteria that look sensible on paper and exclude the patients who exist, schedules no site can run: problems found after first patient in become amendments, and amendments cost months.
02
Feasibility runs on questionnaires
Site selection still leans on self-reported questionnaires and memory of who enrolled last time. Sites that over-promise are a familiar cause of slow recruitment.
03
The TMF is checked too late
Missing, misfiled and unsigned documents in the trial master file are often found during the pre-inspection scramble, when they are hardest to fix.
OUR APPROACH
Faster documents, unchanged accountability
ICH E6(R3) asks sponsors to take a risk-proportionate, quality-by-design approach and holds them responsible for the computerised systems they use, including validation and audit trails. That framing suits AI well, if it is built in from the start. We design systems whose every output is traceable to its inputs, whose every action is logged, and whose decisions are left to the people GCP already names: investigators for eligibility, sponsors for protocol content, the TMF owner for completeness.
We work on documents and operational data, not on patient-level clinical decisions. Where patient data is in scope, as in pre-screening support, it stays on infrastructure under the controller’s control, with GDPR Article 9 and the trial’s consent terms setting the limits. Whether a system needs validation, and to what depth, is your quality function’s call.
HOW IT DEPLOYS
PHASE 01
Pick one document flow
Protocol, feasibility or TMF: we start where the delay is measurable and the data is accessible.
PHASE 02
Integrate, do not replace
Agents read and write through your CTMS, eTMF and EDC, such as Veeva Vault or Medidata, rather than adding another system to validate.
PHASE 03
Validate proportionately
Versioned models, test sets from your past studies and full audit trails, so your quality team can validate to the risk.
What we build
01
Trial Protocol Drafting
Protocol drafts built to the ICH M11 structured template from your synopsis, prior protocols and the literature, with eligibility criteria checked against real-world feasibility.
SEE PROTOCOL DRAFTING →02
Site Feasibility & Selection
Sites ranked on registry history, investigator track record and your own enrolment data, with the evidence for each ranking shown and the choice left to your team.
SEE SITE FEASIBILITY →03
Trial Document QC
Continuous checks of the eTMF against the TMF Reference Model: misfiled, missing, unsigned and out-of-date documents raised for a person to resolve.
SEE TMF QC →04
Evidence Extraction
Cohorts, endpoints and adverse events extracted from published trial reports with a citation on every value, for comparator and background sections.
SEE EVIDENCE EXTRACTION →Controls we implement
GDPR ART. 9-ALIGNED CONTROLS
AUDIT TRAIL ON EVERY ACTION
VERSIONED MODELS AND PROMPTS
NO TRAINING ON YOUR DATA
ROLE-SCOPED ACCESS
These are engineering controls we build into the architecture, not certifications. QQuantum.ai holds no security or GxP certifications, and system validation and inspection readiness remain yours — see our trust centre.
Clinical trials use cases
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CRO.02
Site Feasibility & Selection
Sites ranked on registry history, your own enrolment data and population fit, back-tested on completed studies, with the feasibility team making the selection.
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CRO.03
Trial Document QC
Continuous eTMF checks against the TMF Reference Model for misfiled, missing and unsigned documents, each raised for a person to fix; the system never changes a record itself.
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Questions
Questions about Clinical Trials & CROs.
Can AI be used in clinical trials?
Yes, and it already is, in protocol design, site selection, document management and pre-screening support. The regulatory expectation is that the sponsor remains responsible, that computerised systems are fit for purpose and validated proportionately to risk, and that records are attributable and auditable. The EMA and FDA have both published reflections and draft guidance on AI across the medicines lifecycle, and ICH E6(R3) sets the GCP frame.
Will AI take over clinical trials?
No. Trials depend on investigators examining patients, ethics committees weighing risk, monitors visiting sites and sponsors answering for the data. AI takes over parts of the paperwork and analysis that surround those roles: drafting, cross-checking, ranking and searching. Each of those outputs still lands with a named person who decides and signs.
What AI tool can be used for clinical research?
Commercial CTMS, eTMF and EDC platforms increasingly ship AI features, and general assistants help with reading. Gaps appear where you need your own protocols, site history and quality rules combined, or where data cannot leave your control. That is the work we do: systems built on your existing platforms and data, rather than one more tool beside them.
Is AI in clinical trials regulated?
The trial is regulated, and so are the systems that support it. In the EU, the Clinical Trials Regulation 536/2014 and GCP govern the trial, GDPR governs patient data, and the EU AI Act may apply depending on the system’s use. In the US, 21 CFR Part 11 covers electronic records. Which obligations bind a given system is your regulatory team’s determination.
Does AI help with patient recruitment for clinical trials?
It can help sites find candidates by matching eligibility criteria against records the site already holds, inside the site’s own systems. It does not decide eligibility; the investigator does. We build pre-screening that suggests and explains, keeps patient data on the controller’s infrastructure, and logs every match for review.
