POSTPONED: Let’s Learn AI Series - Digital Oncology & AI
This event has been postponed due to weather. Registered participants will be notified once a new date is scheduled. As part of YSPH’s Strategic Plan Priority #4 on Data Science and AI, we’re launching a new seminar series “Let’s Learn AI.” The series will equip our community to effectively engage with the foundational, applied, and ethical dimensions of AI and prepare us to use modern AI tools and methodologies with confidence. Please join us for the first seminar in this series.
Abstract
Digital oncology is increasingly leveraging artificial intelligence and real-world data to address critical challenges in cancer research and care. This seminar presents key technologies developed to improve access to oncology clinical trials and to identify patients at high risk of disease recurrence. An AI-driven clinical trial patient matching platform is described that applies natural language processing to electronic health record data to translate complex eligibility criteria into computable phenotypes, enabling scalable and efficient trial screening. The seminar also introduces ATIR (AI to Identify High-Risk Recurrence), a breast cancer–focused system that integrates clinical, pathology, and treatment data to stratify recurrence risk and support trial prioritization. Drawing on real-world implementations, this talk highlights how digital oncology technologies can bridge research and clinical practice, enhance trial enrollment, and support precision oncology through data-driven risk assessment.
About the Speaker
Dr. Guannan Gong brings over a decade of experience at the intersection of healthcare information technology and clinical research. He began his career as a healthcare informaticist with technical roles at Epic Systems and InterSystems Corporation—two of the leading companies in the healthcare IT sector. At Epic, Dr. Gong contributed to the development of electronic health record (EHR) infrastructure that supports clinical workflows at major U.S. hospitals. He then joined InterSystems, where he worked on health information exchange platforms and clinical data integration systems used worldwide. These roles equipped him with deep expertise in health data architecture, interoperability, and real-world clinical operations.
Dr. Gong later transitioned to academia, where he earned a PhD in Computational Biology and Bioinformatics at Yale University under Dr. Harlan Krumholz. His research has focused on next-generation digital phenotyping and real-world data (RWD) analytics to support precision medicine and clinical trial optimization. This research laid the foundation for a series of digital health initiatives, including the development of AI-powered tools for automated patient screening, clinical trial matching, risk identification, and healthcare equity and outcome research. His digital phenotyping approach has been applied across numerous oncology studies, leading to multiple peer-reviewed publications and successful grant-funded collaborations with leading cancer researchers at Yale Cancer Center and beyond.