# OncoMatch

- **Event:** [The Future of Agentic AI in Healthcare - Abridge x Anthropic x Lightspeed](https://cerebralvalley.ai/e/abridge-hackathon)
- **When:** Sat, Jul 18 at 9:00 AM – 10:00 PM (PDT)
- **Where:** San Francisco, CA
- **Team:** [Sahithi Chimmula](https://cerebralvalley.ai/u/Sahithi_c), [Snehal Hattikal](https://cerebralvalley.ai/u/Snehal_Hattikal)
- **GitHub:** https://github.com/sahithic2000/oncology_clinical_trial_matching_agent/tree/main
- **Demo video:** https://drive.google.com/file/d/1Y4BIIIVImmw81IpkdNueabc4KvBCZ-kV/view?usp=sharing
- **Gallery:** https://cerebralvalley.ai/e/abridge-hackathon/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/abridge-hackathon/hackathon/gallery/111

OncoMatch is an AI-powered clinical trial pre-screening agent that helps oncology care teams quickly identify and prioritize relevant clinical trials for a patient.

Today, finding the right clinical trial is highly manual. A clinician or research coordinator may need to search through many studies and read long, complex eligibility criteria, then compare those requirements against scattered patient information such as diagnosis, biomarkers, treatment history, performance status, lab values, and imaging findings.

OncoMatch automates this first-pass screening process.

It takes a patient’s clinical context, searches for relevant oncology trials, converts complex eligibility criteria into structured rules, and evaluates the patient against those criteria using a hybrid AI + deterministic approach. Clear criteria such as age, ECOG status, and lab thresholds are evaluated with deterministic rules, including unit normalization. More nuanced criteria—such as biomarker interpretation, disease terminology, prior treatment context, or active versus treated brain metastases—are evaluated using Claude with supporting patient evidence.

The system then ranks trials as High, Moderate, or Low Potential Match and explains exactly why each criterion passed, failed, is unknown, or needs clinical review.

What problem does it solve?

The core problem is:

The right clinical trial may exist, but determining whether a specific patient could qualify is slow, complex, and difficult to scale. OncoMatch reduces the manual burden of reviewing trial criteria and helps care teams focus first on the most promising trials—without pretending to make the final eligibility decision.

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Markdown version of https://cerebralvalley.ai/e/abridge-hackathon/hackathon/gallery/111. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt