AI and Big Data: A New Era in Cancer Care and Research
Don’t miss NCI’s Dr. Warren Kibbe, University of Colorado Cancer Center’s Dr. Christopher Lieu, and University of California San Francisco’s Dr. Travis Zack, as they talk about the current and future applications of artificial intelligence (AI) and big data in oncology. If you attend this session, you’ll also gain practical knowledge on integrating technologies into your research and clinical practices.
This session is part of the 2024 Association of American Cancer Institutes (AACI)/Cancer Center Administrators Forum (CCAF) Annual Meeting in Chicago, IL. If you’re already attending this paid conference, be sure to check out their presentation!
Explore the full agenda on the event website!
Dr. Kibbe is the inaugural NCI deputy director for data science and strategy, working across NCI, NIH, and with other agencies, academia, and industry to define the data science strategy for cancer research. He has been a proponent for open science and open data in biomedical research and helped define the data sharing policy for the NCI Cancer Moonshot SM program. Jointly with Sorena Nadaf, they founded the Cancer Informatics for Cancer Centers (Ci4CC.org) society. He also co-leads the NCI Childhood Cancer Data Initiative.
Dr. Lieu is a professor of medicine in the Division of Medical Oncology, and associate director for clinical research at the University of Colorado (UC) School of Medicine in Aurora, CO. Currently, he’s the associate director for clinical research and director of GI medical oncology at the UC Cancer Center; he’s the co-chair of NCI’s Colon Cancer Task Force; he serves on the FDA Oncologic Drugs Advisory Committee; he serves on the National Comprehensive Cancer Network (NCCN) Panel for Neuroendocrine Cancers; and he’s the vice-chair of the NCCN Board of Directors.
Dr. Zack is a GI medical oncologist, whose research centers on developing and ethically applying the latest advances to improve the lives of cancer patients. He uses AI to maximize how researchers learn from and synthesize the information in a patient’s medical records and from their tumor’s genetic mutations to precisely personalize care.
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