Join researchers from RTI International for an insightful webinar exploring the transformative impact of AI in healthcare, informatics, and the research community. We'll kick off with a brief overview of AI's role in the sector, highlighting its integration with Clinical Decision Support (CDS) systems, applying AI in Large-scale EHR Data Repositories, and how LLMs are more than just Gen AI. Following this, we'll delve into two practical application examples, showcasing AI's real-world benefits. The session will conclude with an examination of AI governance, ensuring ethical and effective implementation, and an engaging Q&A segment. Don't miss this opportunity to learn from experts and gain valuable insights into the future of AI in healthcare.
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Learning Objectives
- Understand the Responsible Use of AI in Healthcare through Real-world AI Applications
- Explore Applications of AI in Large-scale EHR Data Repositories
- Discover the Capabilities of Large Language Models (LLMs) for Novel Approaches to Text Analysis
By the end of this webinar, participants will have a comprehensive understanding of AI's current and potential future roles in healthcare, practical insights from real-world applications, and knowledge of the governance frameworks essential for ethical AI implementation.
Presenters
Laura Marcial, PhD, FAMIA
Laura Marcial is a Health Informaticist who has been working with AMIA for ~21 years. Laura is the Senior Director for the Center for Informatics at RTI International. The center has ~50 staff and includes researchers in Bioinformatics, Environmental informatics, and Health informatics.
Jamie Pina , PhD, MSPH
Jamie Pina is a public health informatician with 20 years of experience in the development, implementation, and evaluation of information systems to support public health practice and research. Jamie is the Scientific Director of Public Health Technology at RTI International.
Daniel Brannock, M.S.
Daniel Brannock is a research data scientist in the Center for Data Science and AI at RTI International, which uses modern data science techniques to progress RTI’s mission to improve the human condition. He has experience leveraging machine learning, Bayesian modeling, clustering, optimization, forecasting, and simulation across a broad array of industries and applications. His research focus is in applying data science in health care data and applications.
Emily Hadley, M.S.
Emily Hadley is a research data scientist at RTI International focusing specifically on responsible AI. In her work, Emily collaborates with subject matter experts to solve complex problems in health, education, and criminal justice using AI, data science, and statistical approaches. Emily’s main research interests are exploring technical and policy approaches to addressing bias, equity, and ethics in data science and AI. Emily leads RTI’s contributions to the NIST AI Safety Institute Consortium. She has presented at numerous conferences, including IEEE Big Data; ACM Fairness, Accountability, and Transparency (FAccT); and the Joint Statistical Meetings (JSM). She has several data science publications and has been quoted about data science research in Scientific American and the New York Times. Emily holds a BS in Statistical Sciences with a second major in Public Policy from Duke University and a MS in Analytics from North Carolina State University.
