Application of Artificial Intelligence & Machine Learning for Precision Medicine

As compared to traditional statistical or pharmacometrics modeling, Artificial Intelligence (AI)/Machine Learning (ML) excels at handling high dimensional data and often has superior predictive capability, making it a powerful tool for prediction of clinical outcome, including disease prognosis and/or treatment response (for both efficacy and safety) based on patient characters as well as treatments information. AI/ML has potential to improve the efficiency of drug development and advance precision medicine, but also present challenges such as bias, issues with generalizability, opacity, disparity, fairness and trustworthiness.

Meeting Objectives

1. Review progress made from implementing artificial intelligence and machine learning in drug development and precision medicine. 
2. Discuss methodologies and best practices used today in this field
3. Discuss technical challenges such as bias, generalizability, and opacity and how they can lead to issues of data disparity, fairness, and trustworthiness. We will also discuss how to address these challenges.

View the agenda online

The workshop which is jointly sponsored by the University of Maryland Center for Excellence in Regulatory Science and Innovation (M-CERSI) and the FDA, and was open to the public.

This workshop is supported by the Food and Drug Administration (FDA) of the U.S. Department of Health and Human Services (HHS) as part of a financial assistance award U01FD005946 with 100 percent funded by FDA/HHS. The contents are those of the author(s) and do not necessarily represent the official views of, nor an endorsement, by FDA/HHS, or the U.S. Government.