Clinical trials depend on accurate cohort selection and the ability to adjust based on emerging data. In practice, this is difficult because relevant data is distributed across clinical systems, trial management platforms, EHRs, genomic datasets, and external research sources.
Teams spend significant time assembling cohorts using incomplete or delayed data. Patient eligibility criteria evolve, but updates are not consistently reflected across systems. Trial designs are often locked early, even as new signals emerge during execution.
This creates several issues:
Cohorts may not reflect the most relevant patient populations
Enrollment timelines extend due to manual screening and data gaps
Trial adjustments are slow and require coordination across teams
Insights from ongoing trials are underutilized in real time
Data exists, but it is not continuously connected or operationalized within the trial workflow.
Syntes AI provides a unified context layer for clinical and research data, enabling continuous cohort evaluation and adaptive trial workflows.
The platform connects patient data, clinical records, trial protocols, genomic information, and external research into a live knowledge graph. This creates a structured, real-time representation of patients, eligibility criteria, and trial conditions.
AI agents operate on this shared context to support cohort stratification and trial design decisions as data evolves.
Instead of relying on static cohort definitions, the system continuously evaluates patient populations, identifies eligible participants, and updates recommendations based on new information.
Unified Clinical and Research Context
Patient records, trial criteria, biomarkers, and historical trial data are connected into a single, continuously updated model.
Continuous Learning and Memory
Enrollment outcomes, cohort performance, and trial results are retained and used to refine future cohort selection and trial adjustments.
Agent-Supported Cohort and Trial Management
AI agents support trial teams by:
Identifying eligible patients based on evolving criteria
Stratifying cohorts using clinical, demographic, and genomic signals
Monitoring cohort performance and enrollment progress
Recommending adjustments to inclusion criteria or cohort structure
Supporting adaptive trial design based on interim results
All recommendations are grounded in real data and aligned with trial protocols.
Clinical teams gain a more responsive and data-driven approach to trial design and execution.
Cohort identification becomes faster and more precise
Enrollment improves through better matching of patients to trials
Trial designs can adapt based on real-time data
Operational effort is reduced across research and clinical teams
Decisions are supported by clear data lineage and audit trails
The result is a trial process that evolves with the data, supported by systems that maintain context and assist with decision-making throughout the lifecycle.
