Abstract: 

The rapid digitization of healthcare has accelerated the adoption of artificial intelligence for clinical applications. A major opportunity for predictive clinical analytics is the ability to provide faster diagnoses and treatment plans tailored to individuals. Applications have been developed for multiple care settings, with many novel point-of-care diagnostics that can assess for diseases ranging from malaria to skin cancer. In this talk, we will demonstrate how to apply machine learning algorithms to identify regions of interest for detection of the pupil for point-of-care drug screening. We will highlight state-of-the-art technology to support real-time image analysis, approaches for the use of real-world clinical and patient-generated data, and best practices for translating novel AI algorithms from controlled research and development environments into real-world, clinical settings. Finally, we will review current regulatory requirements for training, validating, and providing ongoing quality assurance for algorithms that are deployed to clinical settings. Attendees will gain an understanding for good machine learning practices, be able to describe approaches to develop high-quality clinical data sets, and design practical validation plans to ensure algorithm generalizability. As healthcare continues to evolve into a digital enterprise where data are reagents and software is the analytic engine, an understanding of best practices to develop, implement, and assess clinical predictive models is critical for the safe and rapid deployment of AI in healthcare.

Bio: 

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