The Gong Lab
Yale School of Medicine · Yale Cancer Center
AI for oncology trials, real-world evidence, and precision cancer care
We build clinical informatics and machine learning systems that match patients to trials, structure eligibility at scale, and turn EHR data into actionable research.
Research focus
Four interconnected areas where we develop methods and deploy them in cancer care.
Clinical trial patient matching
Real-time OMOP-based prescreening across structured and unstructured EHR data.
Eligibility criteria intelligence
NLP and LLM pipelines to structure, cluster, and visualize trial criteria at scale.
Real-world data & EHR science
Computational phenotyping, data integrity, and predictive modeling for research.
Equitable trial access
Methods designed to reach underserved and underrepresented patient populations.
From methods to impact
Led by Guannan Gong, PhD, the lab integrates electronic health records, clinical NLP, large language models, and trial evidence synthesis. Our research informs CtrlTrial—translating lab work into tools for real-time clinical trial recruitment at Yale and beyond.
- 2026 — CTPM validated across 29 oncology trials (JCO Clinical Cancer Informatics)
- 2026 — Underrepresentation study across three NCI-designated cancer centers (JCO Oncology Advances)
- 2025 — Blavatnik Accelerator Award for AI-powered trial matching
