AI methods have reached and even surpassed human-level accuracy in numerous areas of healthcare. However, adoption of these technologies into clinical workflows, where interpretability is of paramount importance, is slower compared to other industries. In this talk, we will present an overview of our research in improving the interpretability of AI models in medical image analysis through counterfactual examples and radiologist gaze data collection.
About the Speaker
Dr. Tasdizen is a Professor in Electrical and Computer Engineering and the Scientific Computing and Imaging (SCI) Institute at the University of Utah. His areas of expertise are medical image analysis and machine learning.
Also, check out the Medical Imaging with Deep Learning Conference: https://2025.midl.io/
Recorded on Sept 19, 2024 at the Visual AI in Healthcare virtual event.
Top comments (2)
I've been following how interpretable AI models are transforming radiology, and I believe they're making medical imaging more transparent and reliable by helping clinicians understand how AI reaches its conclusions. As someone interested in the future of healthcare, I also see growing opportunities in diagnostic radiologist jobs, where professionals can combine their expertise with advanced AI tools to deliver faster, more accurate patient care while maintaining confidence in every diagnosis.
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