Use Case
Cleveland Clinic & Dyania — clinical-trial screening
Documented application · Greek-connected international deployment
What the evidence establishes
Cleveland Clinic describes using Dyania Health’s Synapsis AI to support identification of patients for clinical trials. Its October 2026 update states that enterprise deployment began in November 2025.
Results and their limits
Cleveland Clinic reports that study teams enrolled 40% more patients, 47% faster on average. These are organisation-reported operational results and are not evidence of treatment efficacy.
How to approach a comparable workflow
A comparable research workflow would compare machine-assisted screening with trained reviewers on agreed eligibility criteria. Record why a record was flagged and retain clinical review of eligibility.
The following is an AIStrategy.gr implementation proposal, not a description of undisclosed project architecture. Begin with a bounded task, a named operational owner and an explicit acceptance decision. Separate data preparation, model output and the final business action so each can be evaluated.
Data and operating prerequisites
Document the origin, permissions, update frequency and known gaps in the input data. Build a representative evaluation set before changing the workflow. Include difficult cases and examples that should be escalated rather than answered automatically.
Risks and human oversight
False exclusions can deny research opportunities, while false matches create extra work. Site permissions, patient-data governance and population-specific validation are essential.
What to measure
Measure sensitivity for eligible patients, reviewer agreement, screening time, exclusions and performance across relevant cohorts. Recruitment speed alone is insufficient.
A practical first pilot
- Define the task, affected users and what a correct outcome means.
- Record the current baseline and build the evaluation sample.
- Run in advisory mode with staff reviewing the outputs.
- Review quality, effort, cost and incidents together before expanding.
Decision gate
Proceed only when the responsible team can explain which errors remain, who handles them and whether measured benefit survives the cost of operation and review. Revise or stop the pilot when that case is not established.
Source and review
Source reviewed 6 November 2026. Results are attributed to the publishing organisation or supplier. AIStrategy.gr has not independently audited them.
Use the practical playbooks ↗ · Explore the discussion questions ↗ · Submit evidence or a correction ↗