| Speaker: | Spies is a medical director of the Applied AI group within the Institute for Research and Innovation at ARUP laboratories, and an assistant professor at the University of Utah. His research focuses on how we can use data analytics and artificial intelligence tools to improve our quality control and reduce the impact that laboratory errors have on patient care. |
| Overview: | This presentation will cover the key concepts and terminology surrounding validating, implementing, and monitoring AI solutions in the clinical laboratory. It intends to serve as a guide for laboratorians as they begin designing comprehensive validation efforts, engaging in multidisciplinary implementation projects, and establish ongoing quality management systems to ensure that these solutions remain safe and effective. |
| Learning Objectives: | At the conclusion of this session, participants will be able to: 1) Define key roles and responsibilities in the machine learning life-cycle. 2) Explore techniques for validating, deploying, and monitoring models. 3) Reinforce these concepts within a relevant, lab-based example. |
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Watch Webcast (Feb 19, 2026)
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