TITLE: Opportunities and Challenges in the AI Age: Concrete Examples from Statistics, Science, and Beyond
SPEAKERS: Mark Chang and Niya Xiong, Boston University
MODERATOR: Din Chen
Abstract
Artificial intelligence is rapidly transforming statistics, science, medicine, engineering, and many other disciplines. As AI systems increasingly automate routine analytical tasks, the ability to think critically, reason creatively, and draw insightful analogies across domains is becoming more valuable than ever. At the same time, AI is raising profound questions about scientific reasoning, causality, decision-making, and even the nature of understanding itself.
This tutorial explores both the opportunities and challenges of the AI era through a collection of concrete examples drawn from pharmaceutical statistics, clinical development, scientific discovery, philosophy of science, and human reasoning. On the opportunities side, participants will learn how AI and machine learning can improve clinical trial efficiency, enable precision medicine, support adaptive designs, and generate innovative frameworks such as PROCOVA, PREDCOVA, and Split-Cross PROCOVA for traditional and adaptive designs. The tutorial will also illustrate how statistical thinking, randomization, and game-theoretic reasoning can rescue seemingly failed development programs and create new pathways for innovation.
On the challenges side, the tutorial examines a series of thought-provoking paradoxes and controversies that challenge conventional wisdom. Examples include intransitivity in rank-based decision making, the tension between causality and free will, the distinction between discovery and invention, the reliability of mathematical foundations, and hierarchical evolution and the identity paradox. Together, these examples raise deeper questions about scientific reasoning, the nature of understanding, and the circular definitions of survivorship and fitness.
The tutorial concludes by discussing the future relationship between humans and AI, including the concepts of Humanized AI, large-data versus small-data learning paradigms, and the role of human judgment in an increasingly AI-driven world. The goal is not merely to showcase AI technologies, but to encourage participants to think more deeply about science, knowledge, reasoning, and the opportunities and challenges that lie ahead.
Opportunities in Pharmaceutical Statistics and Beyond
- PROCOVA: A Regulatory-Embraced AI-Assisted ANCOVA Framework.
- PREDCOVA: Precision-Medicine-Based PROCOVA with Similarity-Based Machine Learning.
- Split-Cross PROCOVA: A Further Enhanced PROCOVA Framework.
- AI-Enabled Adaptive Clinical Trial Designs.
- The Power of Randomization in Rescuing Failed Drug Development Programs.
- Game-Theoretic Approaches to Clinical Development Programs (CDPs).
Challenges and Controversies in Statistics, Science, and Beyond
- Intransitivity in Rank-Based Decision Making.
- Hierarchical Evolution and the Identity Paradox: When One Entity’s Evolution Is Another Entity’s Devolution—and the Circular Definitions of Survivorship and Fitness.
- Can Mathematics Be 100% Reliable? Proof by Contradiction and the Limits of Certainty.
- Discovery versus Invention: Which Drives Scientific Progress?
- The Multi-Faceted Objective World: Causal Determinism versus Free Will.
- The Nature of Understanding, Meaning, and Emotion.
- Humanized AI: Large-Data-Based versus Small-Data-Based Models.
Suitable for participants at all levels, this tutorial combines critical thinking, creative analogies, innovative yet practical applications, thought-provoking examples, and interdisciplinary perspectives that will be of interest to statisticians and professionals throughout the pharmaceutical and biotechnology industries.
Reference Books:
- Mark Chang (2025). Critical Thinking and Creative Analogies in Statistics, Sciences and Technology – Essential Skills for the AI Era. CRC Press.
- Mark Chang (2023). Foundation, Architecture and Prototyping of Humanized AI
- Mark Chang (2020). Artificial Intelligence for Drug development, Precision Medicine, and Healthcare. Chapman and Hall/CRC
Instructors’ Biography:
Dr. Mark Chang is Sr. Vice President, Strategic Statistical Consulting at Veristat. Before joining Veristat, Chang was Vice President of Biometrics at AMAG Pharmaceuticals. Chang is a fellow of the American Statistical Association and an adjunct professor of Biostatistics at Boston University. He is a co-founder of the International Society for Biopharmaceutical Statistics, co-chair of the Biotechnology Industry Organization (BIO) Adaptive Design Working Group, and a member of the Multiregional Clinical Trial (MRCT) Expert Group. Chang has served associate editor for Journal of Pharmaceutical Statistics. He has been invited to serve as a co-chair on the scientific advisory board and organization committees for several national and international professional/academic conferences on statistics and clinical trial designs. He has given over 50 lectures, short courses, and invited speeches at national and international conferences. Dr. Chang is an expert in adaptive clinical trials and other innovative approach in drug development and has been invited to present adaptive design and biomarker topics at the US Food and Drug Administration. Chang has broad research interests. He has published 10 books, including Adaptive Design Theory and Implementation Using SAS and R, Paradoxes in Scientific Inferences, Modern Issues and Methods in Biostatistics, Monte Carlo Simulation for the Pharmaceutical Industry, Principles of Scientific Methods, and Innovative Strategies, Statistical Solutions and Simulations for Modern Clinical Trials.
Niya Xiong is a Ph.D. student at Boston University.