Speaker: Dr. Zihao Wang
From: University of Tennessee at Chattanooga
Abstract
The growing integration of artificial intelligence (AI) into healthcare holds immense promise, from accelerating diagnostics to personalizing treatments. However, are we truly ready to deploy AI in real-world medical settings where data is scarce, noisy, and imperfect? The answer is no—there are still critical challenges to overcome. In this talk, I will delve into the complexities of applying AI to medical imaging, focusing on the use of generative models for precision medicine and cross-modality image computing. I will present our recent advances in unsupervised learning, Bayesian shape inference, and AI-driven cochlear implant planning, designed to address the unique challenges of clinical environments. The discussion will conclude by exploring the open questions around trust, robustness, and fairness in AI systems to make AI-powered healthcare accessible and reliable for all.
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