About the Symposium
Where will the AI revolution take Pharmaceutical Science?
The Pfizer/ Northeastern/ASA Symposium on Risks and Opportunities of AI in Pharmaceutical Medicine (AIPM) is an event jointly sponsored by Pfizer Inc., the Roux Institute at Northeastern University and the American Statistical Association (ASA).
Our world increasingly relies on data and computing to create knowledge, to make critical decisions, and to better predict the future. Data science has emerged to support these data-driven activities by integrating and developing ideas, concepts, and tools from computer science, engineering, information science, statistics, and domain fields. Data science now drives fields as diverse as biology, astronomy, material science, political science, and medicine—not to mention vast tracts of the global economy, key government activities, and quotidian social and societal functions.
The pharmaceutical enterprise has been slower to respond, especially to the rapid developments in AI, but tectonic shifts are underway in approaches to the discovery, development, evaluation, registration, monitoring, and marketing of medicines for the benefit of patients and the health of the community.
While there is much discussion about the potential of AI and modern machine learning tools to transform the drug development paradigm, there is a growing recognition of the paucity of research about the opportunities, inevitable pitfalls and unintended consequences of the digital revolution in this important area of application. As we move toward personalized and truly evidence-based medicine, the use of AI and machine learning to optimize drug deployment raises a whole different set of challenges.
This forum is, therefore, expected to serve as a platform for distinguished statisticians, data scientists, regulators, and other professionals to address the challenges and opportunities of AI in pharmaceutical medicine; to foster collaboration among industry, academia, regulatory agencies, and professional associations; and to propose recommendations with policy implications for proper implementation of AI in promoting public health.
About the Sponsors
The Center for Targeted Machine Learning and Causal Inference (CTML) at UC Berkeley is an interdisciplinary research center for advancing, implementing and disseminating statistical methodology to address problems arising in public health and clinical medicine. CTML brings the rigor and power of statistical theory together with advances in machine learning and causal inference to generate robust evidence for advancing health.
Based in Washington, DC, and operating under the auspices of the UC Berkeley School of Public Health, the Forum for Collaborative Research is a public/private partnership addressing cutting-edge science and policy issues through a process of stakeholder engagement and deliberation.