Amidst COVID, healthcare has accelerated into the Age of Implementation for Artificial Intelligence (AI). From bench- to bedside and across multiple therapeutic areas, AI is generating insights and value.   At Lilly, we continue to realize that three critical strategies for success are a platform of platforms approach for data and analytics, collaboration across the private and public sector and investment in the start-up ecosystem that fuels innovation.  Our progress to date confirms that lack of understanding around implementation details and the siloing of AI limits its impact. In this brief talk, I will share Lilly Research Labs approach to using AI to enable deeper understanding of human biology anchored around delivering value to, with and for our therapeutic areas.  Join me as we ignite a thought-provoking dialogue epitomized in a formidable saying by Biologist, E.O. Wilson (1999), ‘We are drowning in information, while starving for wisdom.

Timely confirmation of clinical diagnosis shortens the ‘diagnostic odyssey’ facilitating definitive management and counseling. The advancement of sequencing technology, next-generation sequencing, has generated big data and recently propelled genetic laboratory diagnosis. However, challenges in the clinical utilization of genetic tests in precision medicine remains. Leveraging machine learning will bridge identified gaps from data to insight. Solutions in handling clinical and sequencing data in a large reference laboratory will be shared.

Dr. David Fajgenbaum will share his groundbreaking work to advance precision medicine for Castleman disease, a condition that he is battling as a physician, researcher, advocate, and patient. Combining omic technologies with machine learning and other bioinformatic tools, Dr. Fajgenbaum has discovered novel predictive biomarkers of treatment response and novel treatment approaches, including one that is saving his life and others. More information at www.med.upenn.edu/CSTL, www.CDCN.org, and www.ChasingMyCure.com.

Adopting a three-pronged approach to research data in Cell Therapy (CT): 1) consistent metadata within ELN/LIMS system; 2) application of standard ontology; 3) conformance of CT data from all major research systems and semantic integration of CT data with clinical trials data, RWD, scientific literature and other sources. The CT data ecosystem also includes a streamlined way to manage instrument data files and implements visualization tools to better understand experimental results.

The path from collecting large and diverse datasets to enabling precision medicine requires tackling numerous challenges, only a few of which are technical. This talk will discuss some of the outstanding challenges related to data integration and standardization, as well as around building an ecosystem for breaking down siloes of data, talent, and expertise to enable collaboration both within an organization and with external stakeholders.

The speaker will highlight the convergence of data sources and evidence streams, advanced analytics, and innovative technologies and their potential impact on therapeutic development. The speaker will shed light on how regulatory organizations are managing the potential opportunities and challenges. The speaker will mention the U.S. FDA Real-World Evidence (RWE) Program and other relevant initiatives and activities.

Clinical and research information resides in multiple data silos like patient registries, natural histories, bio, and image banks, EHRs, -omics’, and clinical trials datasets. Identifying information on the same individuals across clinical research continuum is challenging, as each data collection utilizes its own participants’ identifiers.