AI + Cancer Research

AI Day for Cancer Research 2021

Ji-Hyun Lee, DrPH

The Cancer Center is diving into the world of Artificial Intelligence (AI), through some recent initiatives. AI is rapidly reshaping cancer research — it is exploding in biomedical research and health care across all dimensions of cancer research where the potential applications for AI are vast.

On September 21, the Cancer Center hosted the first annual AI Day for Cancer Research, which highlighted current AI powered cancer research and provided practical information for researchers interested in utilizing the fast-developing technology. Additionally, the event promoted matchmaking, information sharing and synergistic cross-border collaboration. The event hosted 32 attendees in person and 134 attendees virtually.

“While this strong initiative for AI across all health sectors we — the Division of Quantitative Sciences and its members — felt we needed a clear vision on the AI and its application in our Center’s research,” said Ji-Hyun Lee, DrPH., director of the Division of Quantitative Sciences. “I felt the lessons from other academic and research groups who have had extensive experience employing AI in the research environment were very valuable and helpful for the Center members and Center’s leadership.”

AI Research Day

During AI Research Day, the presenters’ posters were presented in person and virtually by providing a pre-recorded presentation. Throughout lunchtime, in-person participants visited the posters and got to know each other. All attendees were junior faculty or graduate students.

“I was super pleased to get to know trainees from various colleges are working on challenging but exciting AI fields,” said Lee.

New Cancer AI Working Group

Co-led by the Biostatistics and Quantitative Sciences Shared Resource in the Division of Quantitative Sciences and the Cancer Informatics Shared Resource at the UF Health Cancer Center, a new Cancer AI Working Group recently launched.

Led by Co-chairs Mattia Prosperi, Ph.D., and Qing Lu, Ph.D., the mission of the Cancer AI Working Group is to develop collaborative research, expertise and network capacity in artificial intelligence (AI) at the UF Health Cancer Center. Drs. Prosperi and Lu answered some questions about this new working group.

What are your goals for the Cancer AI Working Group?

To develop collaborative research, expertise and network capacity in artificial intelligence (AI), of the Cancer Center. We aim at advancing AI methods and applications in basic and translational cancer research, bridging interdisciplinary expertise and fostering early career investigators. Our objectives are strategic to the overarching goal of cancer center designation.

What do you think is most important for Cancer Center members to know about this working group, and why should they consider registering?

We will have several interactive activities that we call “AI calisthenics.” The working group will hold regular meetings, which will provide a platform for researchers to interact and disseminate ideas related to AI. The meetings are open to Cancer Center members, postdoctoral fellows, graduate students and UF faculty. By interacting with other working group members, researchers are able to create new collaborations and strengthen ongoing interactions, facilitate submissions of AI-related manuscripts and grant applications. By registering as working group members, Cancer Center members and their postdoctoral fellows/graduate students will also receive information regarding AI-focused seminars, symposia and travel awards. There will be opportunities for pilot funding —we will work with early career scientists and new UF hires to connect people across different departments.

AI is an emerging topic in health care at the moment, especially at UF. How does this working group help support UF’s AI Agenda?

Cancer care, AI imaging, AI omics and novel therapeutics in addition to supporting the UF AI Agenda by providing information on AI and cancer research method, tools and expertise in an effort to move toward collaboration and team science.

In general, what are some of the most promising AI applications for the study, diagnosis and treatment of cancer?

New deep learning algorithms for image analytics and integration with other lab markers, massive omic data analysis and massive-scale therapeutics screening.