Applying AI/ML Methods to Generate Digital Endpoints in Clinical Trials

Abstract: 

Clinical drug development has historically been a costly and complex task. Advances in AI/ML and digital health promise increased efficiency and reduced costs in drug discovery. They can potentially offer better outcomes in clinical trials by accelerating clinical research and offering more patient-centric care. This talk will provide an overview of AI/ML approaches used to develop, validate, and deploy digital endpoints in decentralized clinical trials and Software as a Medical Device (SaMD) settings.

Bio: 

Tomasz Adamusiak MD Ph.D. is a Chief Scientist in the Clinical Insights & Innovation Cell at MITRE. He leads a multi-disciplinary group driving high-impact contributions to private and public sectors in Clinical and Genomic Data Science. Before MITRE, Tomasz was the Head of Data Science in the Pfizer Innovation Research (PfIRe) Lab. His team was responsible for developing novel digital endpoints, designing decentralized approaches for clinical trials, and applying AI/machine learning methods to generate novel insights from clinical data. Tomasz served in leadership and advisory roles in the American Medical Informatics Association, the SNOMED International, and the Epic Research Data Network.

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