Multi-Agent Clinical Trial Matching System
Evaluate patient eligibility against clinical trial criteria using a simulated agent workflow.
AI-Assisted Clinical Trial Matching Workflow
Clinical Trial Matching Agent is a simulated multi-agent application that evaluates patient eligibility against clinical trial criteria using a structured workflow. It demonstrates how patient context, protocol criteria, recommendation logic, and human review can work together in an explainable screening process.
Problem
Clinical trial screening is slow, complex, and highly manual
Clinical trial screening is slow and complex. Eligibility criteria are nuanced, patient records can be incomplete, and many cases require manual interpretation of inclusion and exclusion rules across multiple systems.
Teams need a faster way to identify likely candidates while preserving transparency, auditability, and clinician oversight.
Solution
A transparent, explainable screening workflow
This system simulates a multi-agent workflow that evaluates a selected patient against an active trial and produces an explainable recommendation. The workflow breaks screening into clear stages with evidence, rationale, and review handling.
The experience emphasizes transparency over black-box scoring. Users can inspect why a recommendation was made, replay workflow activity, review flagged cases, change the active trial, and reset the demo to explore different screening paths.
Overview
Reframing clinical trial screening as a structured workflow
Clinical trial screening is often time-consuming because eligibility criteria are written in protocol language while patient information is spread across multiple structured and unstructured sources. Reviewers need a faster way to assess likely matches without losing transparency into why a patient was recommended or excluded.
This demo presents that challenge as a staged agent workflow. Rather than treating trial matching as a single opaque AI decision, the system breaks the process into patient selection, eligibility evaluation, recommendation generation, evidence review, and human approval.
How It Works
A high-level view of the simulated workflow from candidate selection through review.
A patient is selected from the active trial’s eligible population.
Agents evaluate patient data against inclusion and exclusion criteria.
A recommendation with rationale and confidence is generated.
Criteria-level evidence is presented for transparency.
Reviewers approve, reject, or request more information.
System Architecture
The application combines a modern frontend, API-driven workflow services, and a structured mock data layer.
Agent Workflow
A closer look at the simulated multi-agent workflow that powers each evaluation.
Collects and structures patient data relevant to the active trial.
Parses protocol criteria and converts them into machine-usable rules.
Evaluates patient data against each criterion and determines match status.
Generates the overall recommendation with rationale and confidence.
Makes the final decision or requests more review for ambiguous cases.
What this enables
Turning a demo into a real clinical workflow system
- Demonstrates a more realistic clinical AI product pattern than a single chatbot response.
- Provides transparency through criteria-level evidence and workflow explainability.
- Supports human-in-the-loop review for ambiguous or higher-risk cases.
- Creates a reusable foundation for richer protocol parsing, patient cohorts, and reviewer collaboration.
What this enables
Designing clinical AI systems for transparency and trust
- Clinical AI applications are stronger when recommendation logic is visible, reviewable, and tied to evidence.
- Human-in-the-loop design is essential for ambiguous eligibility cases.
- A staged workflow makes trial matching easier to understand than a single pass/fail output.
- Explainability builds trust with clinicians and accelerates adoption.