The US life science industry and ecosystem have been an unparalleled engine for transformative medical innovation since the passage of the Bayh-Dole Act in 1980. Since then and through 2022, fueled by public sector funding, burgeoning academic research, private sector investment and competition, and regulatory policies and reforms like PDUFA, accelerated approval, the Orphan Drug Act, and the 21st Century Cures Act, FDA approved 1,355 new pharmaceutical therapies, including a host of major therapeutic advances. In parallel, artificial intelligence (AI) and machine learning have become important new tools helping to map the growth in biological science and drive more cost-effective drug discovery and development with reduced risk. This panel asks the question whether AI can be used to understand and ultimately improve how the diversity of patient access correlates with patient outcomes, market adoption and penetration, and the economics of new drug commercialization and the extent to which payers, providers, and industry could or should use these powerful AI tools to mine the wealth of disparate data and optimize the impact of medical innovation. Panelists include perspectives from industry, payers, public health, and data science.