Speaker
Tony Mangino, PhD
From the rapid development and dissemination of AI systems within the general discourse, the discussion within the scientific community now centers on in-house AI tools that promote data security, confidentiality, and domain specificity while also retaining the user-friendliness and sophistication of general purpose AI systems. However, the development of such AI tools, as is necessary with any computational model, requires that a rigorous and empirically-derived process of evaluation and validation be implemented. This session discusses the processes of evaluating AI tools-with a specific focus on clinical prediction models-using both quantitative and qualitative methods. Recommendations for clinical practice, implementation of these AI tools, and future research are provided.
Zoom link: https://duke.zoom.us/j/96158350382
This event is being cross-promoted by the NC BERD Consortium, a collaboration of the CTSA-funded BERD cores at UNC-Chapel Hill, Wake Forest University School of Medicine, and Duke University School of Medicine.