Talk title: Real-world Data, Real-world Problems, and Real-world Opportunities with AI

Talk abstract: The rapid adoption of electronic health record (EHR) systems has enabled access to large collections of real-world data (RWD), reflecting the characteristics and outcomes of patients treated in real-world settings for research purposes. The increasing availability of RWD, combined with advancements in artificial intelligence (AI)—particularly in machine learning (ML) and deep learning (DL)—presents unprecedented opportunities to generate real-world evidence (RWE) to answer a wide range of biomedical and clinical questions. This talk will focus on various applications of AI on real-world data, highlighting the importance of a robust RWD infrastructure, the associated challenges, but also opportunities.

Bio: Biomedical Informatics is an interdisciplinary field, where the central theme is to explore the effective uses of data, information, and knowledge for scientific inquiry, problem-solving, and decision making, motived by efforts to import human health. Bian has a diverse yet strong multi-disciplinary background in data integration, semantic web, machine learning, natural language processing, social media analysis, network science, data privacy, and software engineering. Nevertheless, his expertise and background serve an overarching theme: data science with heterogeneous data, information and knowledge resources.

He currently serves as the Chief Data Scientist and Chief Research Information Officer for UF Health, Division Chief of Biomedical Informatics in Health Outcomes & Biomedical Informatics, Director of the Biomedical Informatics Program for the UF Clinical and Translational Science Institute, Director of Cancer Informatics Shared Resource, and the Chief Data Scientist for the OneFlorida+ Clinical Research Consortium.