Seminars and Events

Artificial Intelligence Seminar

Learning from the Data and the Lessons Learned: Towards Individually-Tailored Healthcare

Event Details

The excitement around biomedical data science and artificial intelligence (AI)-based methods in healthcare have grown significantly given the explosion of available clinical data. The potential now exists to harness increased computing power, electronic health records, and emergent types of observations (e.g., omics, mobile health, imaging, etc.) to examine multiple factors together, with the promise of discoveries advancing our ability to deliver more individually tailored care. Despite this promise, many of AI-based techniques fail to see real-world usage. Myriad (socio)technical and practical barriers exist to use such data-driven analyses, covering a gamut of issues including the quality/utility of the data, reproducibility and generalizability of models, and ethical AI (ETAI). In this talk, lessons learned from different projects involving machine and reinforcement learning are reflected upon, describing key issues the translation of methods into practice and highlighting research challenges and opportunities.

Speaker Bio

Dr. Bui received his PhD in Computer Science in 2000, upon which he joined UCLA. He is the Director of the Medical & Imaging Informatics (MII) Group and holds the David Geffen Chair in Informatics. He is the Senior Associate Director for Informatics at UCLA’s Clinical and Translational Science Institute (CTSI); Associate Director for Medical Informatics at the Institute for Precision Health (IPH); and Co-Director of the Center for SMART Health. He also serves as the Director for UCLA’s Medical Home Area as part of the Graduate Program in Biosciences. Dr. Bui’s research includes informatics and data science for biomedical research and healthcare in areas related to distributed information architectures and mHealth; development, evaluation, and translation of AI-based methods (e.g., machine learning, reinforcement learning) for healthcare; and clinical data visualization. His work bridges contemporary computational approaches with the opportunities arising from the breadth of biomedical observations and the electronic health record (EHR), tackling the associated translational challenges. In particular, his research looks to develop new ways to use both large-scale observational datasets and newer types of observations (e.g., mHealth) to improve screening and diagnosis through more individually-tailored approaches.

YOU ONLY NEED TO REGISTER ONCE TO ATTEND THE ENTIRE SERIES - We will send you email announcements with details of the upcoming speakers.

Register in advance for this webinar:

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The recording for this AI Seminar talk will be posted on our USC/ISI YouTube page within 1-2 business days:

Host: Muhao Chen POC: Pete Zamar