Keynote 1 - Thursday, April 10, 2025
Chair: Wanjie Sun (FDA)
Title: Responsible Innovation in Health AI
Speaker:
Michael J. Pencina, PhD
Chief Data Scientist, Duke Health
Vice Dean for Data Science
Director, Duke AI Health
Professor, Biostatistics & Bioinformatics
Duke University School of Medicine
Keynote Abstract:
Artificial intelligence is revolutionizing every industry, including health. While the breathtaking pace of development promises a new era of innovation, the opportunities will not be fully realized without thoughtful approach to implementation. In this presentation we explore the potential of AI innovation in health, identify principles that should guide its development and implementation and discuss best practices for responsible AI governance and the role of statisticians in these processes.
Michael J. Pencina, PhD, is Duke Health’s chief data scientist and serves as vice dean for data science, director of Duke AI Health, and professor of biostatistics and bioinformatics at the Duke University School of Medicine. His work bridges the fields of data science, health care, and AI, contributing to Duke’s national leadership in trustworthy health AI.
Dr. Pencina partners with key leaders to develop data science strategies for Duke Health that span and connect academic research and clinical care. As vice dean for data science, he develops and implements quantitative science strategies to support the School of Medicine’s missions in education and training, laboratory and clinical science, and data science.
He co-founded and co-leads the national Coalition for Health AI (CHAI), a multi-stakeholder effort whose mission is to increase trustworthiness of AI by developing guidelines to drive high-quality health care through the adoption of credible, fair, and transparent health AI systems. He also spearheaded the establishment and co-chairs Duke Health’s Algorithm-Based Clinical Decision Support (ABCDS) Oversight Committee and serves as co-director of Duke’s Collaborative to Advance Clinical Health Equity (CACHE).
Dr. Pencina is an internationally recognized authority in the evaluation of AI algorithms. Guideline groups rely on his work to advance best practices for the application of clinical decision support tools in health delivery. He interacts frequently with investigators from academic and industry institutions as well as government officials. Since 2014, Thomson Reuters/Clarivate Analytics has regularly recognized Dr. Pencina as one of the world’s “highly cited researchers” in clinical medicine and social sciences, with more than 400 publications cited over 130,000 times. He serves as a deputy editor for statistics at JAMA-Cardiology.
Dr. Pencina joined the Duke University faculty in 2013, and served as director of biostatistics for the Duke Clinical Research Institute until 2018. Previously, he was an associate professor in the Department of Biostatistics at Boston University and the Framingham Heart Study, and director of statistical consulting at the Harvard Clinical Research Institute. He received his PhD in Mathematics and Statistics from Boston University in 2003 and holds master’s degrees from the University of Warsaw in actuarial mathematics and business culture.
Keynote 2 - Friday, April 11, 2025
Chair: Hong Tian (BeiGene)
Title: Leading Science and Clinical Research – Biostatistics in the Age of AI
Speaker:
Haoda Fu, PhD
Head of the Exploratory Biostatistics, CfDA
Amgen
Keynote Abstract:
AI is rapidly transforming society and has emerged as the defining technology of our generation. Its influence spans across industries, with significant implications for scientific research, healthcare, and drug development. In this work, we review the historical progression of AI and its growing role in pharmaceutical research, highlighting how AI-driven methodologies are revolutionizing drug discovery and development processes. As the integration of AI in biostatistics and clinical research deepens, scientists must cultivate essential skills to remain effective in this evolving landscape. We discuss four critical competencies for scientists working in the AI era: AI Mindsets, AI Communication, AI Integration, and AI-Enabled Innovation. Looking ahead, we explore the potential future impact of AI on the pharmaceutical data analytics ecosystem. The convergence of AI with biostatistics presents both challenges and opportunities, requiring a thoughtful balance between leveraging AI’s capabilities and maintaining rigorous scientific and ethical standards. By embracing AI-driven approaches while upholding core statistical principles, the next generation of scientists can contribute to more efficient, data-driven advancements in clinical research. This discussion aims to provide insights into the evolving role of AI in biostatistics and inspire forward-thinking strategies for navigating the intersection of AI and scientific discovery in the pharmaceutical industry.
Dr. Haoda Fu is Head of Exploratory Biostatistics in Amgen, before that he was an Associate Vice President and an Enterprise Lead for Machine Learning, Artificial Intelli-gence, from Eli Lilly and Company. Dr. Haoda Fu is a Fellow of ASA (American Statistical Association), and IMS Fellow (Institute of Mathematical Statistics). He is also an adjunct professor of biostatistics department, Univ. of North Carolina Chapel Hill and Indiana university School of Medicine. Dr. Fu received his Ph.D. in statistics from University of Wisconsin – Madison in 2007 and joined Lilly after that. Since he joined Lilly, he is very active in statistics and data science methodology research. He has more than 100 publi-cations in the areas, such as Bayesian adaptive design, survival analysis, recurrent event modeling, personalized medicine, indirect and mixed treatment comparison, joint modeling, Bayesian decision making, and rare events analysis. In recent years, his research area focuses on machine learning and artificial intelligence. His research has been published in various top journals including JASA, JRSS-B, Biometrika, Biometrics, ACM, IEEE, JAMA, An-nals of Internal Medicine etc.. He has been teaching topics of machine learning and AI in large industry conferences including teaching this topic in FDA workshop. He was board of directors for statistics organizations and program chairs, committee chairs such as ICSA, ENAR, and ASA Biopharm session. He is a COPSS Snedecor Awards committee member from 2022-2026, and also served as an associate editor for JASA theory and method from 2023, and JASA application and case study from 2025-2027
