PREDICTIVE ML · SPEQ SYNTHESIS
Clinical Trial AI Decision Support
AI applied to clinical trial operations, including matching patients to eligible protocols, prioritizing sites and records under risk-based monitoring, and supporting operational decisions across trial conduct.
Because it operates inside GCP-governed activities that affect participant selection and data oversight, its errors reach subject safety, data reliability, and the integrity of the trial rather than mere efficiency.
What a system class is not
An AI system class describes a shape of system, not a product and not an approval pathway. SPEQ does not qualify, validate, or endorse any implementation, and no regulator recognises these classes as a category.
Weighted score 25/32 (decision consequence and model influence weighted most heavily) places this in the high tier.
Computed deterministically from four Context-of-Use factors — decision consequence, model influence, data sensitivity, and change dynamics. A transparent scoping aid, not a validated risk-assessment system.
SCOPE THE ASSURANCE STRATEGY → CSA WORKBENCHUsed by clinical operations and data-management teams to focus monitoring effort and identify candidate participants, influencing where oversight is concentrated and which subjects are surfaced for screening.
It shapes GCP decisions about monitoring focus and participant identification, so a biased or drifting model can leave real data or safety risks unmonitored or skew who is considered for a trial.
human in the loop
Because outputs influence GCP decisions affecting subjects and data integrity, qualified clinical staff must review recommendations, and the model may focus attention but never decide eligibility or oversight on its own.
- Bias in training data can systematically under-surface certain patient populations for matching, undermining equitable access and the representativeness of the enrolled trial population.
- A risk-monitoring model can misprioritize, directing oversight away from a site or record where a genuine data-quality or safety issue is actually developing.
- Model drift across evolving protocols, sites, and populations can degrade performance while the tool continues to appear reliable in routine operation.
- Automation bias may lead teams to over-trust the model’s prioritization and reduce scrutiny of records or sites it ranked low, weakening independent oversight.
- Opaque recommendations make it hard to justify a monitoring or matching decision to a sponsor, ethics committee, or inspector during trial conduct.
- Assess training data and model outputs for bias across patient populations and sites so matching and monitoring do not systematically disadvantage or overlook certain groups.
- Validate the model against representative clinical scenarios and define how its recommendations are used to focus, not replace, human monitoring and eligibility judgment.
- Monitor performance as protocols, sites, and populations change over the life of a trial, and re-qualify when the operational context shifts materially.
- Preserve interpretability and attributable records of recommendations and human decisions so monitoring and matching choices can be defended to sponsors and inspectors.
- Maintain data-integrity and access controls over clinical inputs and model outputs consistent with GCP expectations for records that support trial conduct.
Standards SPEQ maps to this class
AI-governance frameworks
The AI-specific shelf that defines “quality AI” — see Good AI Practice.
SPEQ synthesis — applied AI-assurance judgment to help you scope your own Context-of-Use assessment and validation. Not regulatory guidance, not an AI classification service, and not a substitute for your documented risk assessment.