HAI Weekly Seminar with Akshay Chaudhari
Beyond Image Interpretation in Radiology: Data-Efficient AI for Accelerating MRI Acquisition
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Beyond Image Interpretation in Radiology: Data-Efficient AI for Accelerating MRI Acquisition
The African Olympiad Academy is a world-class high school dedicated to training Africa’s most promising students in mathematics, science, and artificial intelligence through olympiad-based pedagogy.

The African Olympiad Academy is a world-class high school dedicated to training Africa’s most promising students in mathematics, science, and artificial intelligence through olympiad-based pedagogy.
Despite the rapid adoption of LLM chatbots, little is known about how they are used. We approach this question theoretically and empirically, modeling a user who chooses whether to complete a task herself, ask the chatbot for information that reduces decision noise, or delegate execution to the chatbot...
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Despite the rapid adoption of LLM chatbots, little is known about how they are used. We approach this question theoretically and empirically, modeling a user who chooses whether to complete a task herself, ask the chatbot for information that reduces decision noise, or delegate execution to the chatbot...
Recent applications of artificial intelligence (AI) in radiology have focused on image interpretation tasks such as image classification, segmentation, or detection. However, a fundamental challenge in radiology is to acquire these medical images in a safe and efficient manner. New AI techniques have been proposed to solve the inverse problem of image reconstruction wherein only a limited set of measurements are used to reconstruct medical images with high diagnostic quality. Specifically, in this talk, I will describe how physics-guided AI is currently being used to improve the speed of magnetic resonance imaging (MRI). I will further describe how we may eschew requiring large extents of paired datasets required for supervised model training by using novel unsupervised and semi-supervised approaches for accelerated MRI. Beyond data efficiency, these approaches can help mitigate the challenge of distribution shifts for trained models. I will conclude by describing a 1.5TB dataset that we have made publicly available to help evaluate MRI reconstructions with clinically-relevant metrics.
