Given the enormous increase in healthcare data volumes, our ability to effectively share, integrate and analyze is critical to advancing our understanding of the disease and bringing affordable and efficacious treatments to patients. Due to the breadth and depth of the healthcare data across various modalities such as clinical, genomics, imaging and digital sensors, we need to move beyond traditional methods and bring advanced ML/AI implementations to maximally benefit from the richness of the collected data. As part of the drug development life-cycle vast amounts of clinical trials data are collected in order to identify targets of interest, discover biomarkers to stratify patients who could benefit from the drug, and to study the safety and benefit profile of the drug. Furthermore, after the drug is brought to the market its use in a broader population is collected in a wide range of real-world data sources including, but not limited to, electronic medical records, disease registries, health insurance claims​, ​and digital devices. To date the Pharma industry has not leveraged the wealth of this information to deliver truly personalized care for patients.

Development of advanced ​statistical and machine learning​ methodologies combined with the availability of scalable computing environments is fueling a new wave of digitization in Pharma R&D pipelines thereby creating possibilities to discover and develop personalized medicines. This track will invite experts from industry and academia to share their experiences in using AI/ML for Pharma R&D to showcase successful implementations and also lay the roadmap of future methodological and application innovations accelerating use of ML/AI within Pharma research.