Abstract
This talk provides an insightful overview of integrating artificial intelligence (AI) and statistical methods in medical data analysis. It is structured into three key sections: (1) Introduction to Medical Image Analysis: This section sets the stage by outlining the fundamentals and significance of medical image analysis in healthcare, charting its evolution and current applications; (2) State-of-the-Art AI Applications and Statistical Challenges: Here, we explore the impact of AI, particularly deep learning, on medical imaging, and address the accompanying statistical challenges, such as data quality and model interpretability; (3) Opportunities for Statisticians: The final section highlights the critical role of statisticians in refining AI applications in medical imaging, focusing on opportunities for advancing algorithmic accuracy and integrating statistical rigor. The talk aims to demonstrate the crucial synergy between AI and statistics in enhancing medical data analysis, emphasizing the evolving challenges and the vital contributions of statisticians in this domain.
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Dr. Hongtu Zhu is a professor of biostatistics, statistics, radiology, computer science, and genetics at the University of North Carolina at Chapel Hill. He was a DiDi Fellow and Chief Scientist of Statistics at DiDi Chuxing between 2018 and 2020 and held the Endowed Bao-Shan Jing Professorship in Diagnostic Imaging at MD Anderson Cancer Center between 2016 and 2018. He is an internationally recognized expert in statistical learning, medical image analysis, precision medicine, biostatistics, artificial intelligence, and big data analytics. He received an established investigator award from the Cancer Prevention Research Institute of Texas in 2016 and the INFORMS Daniel H. Wagner Prize for Excellence in Operations Research Practice in 2019. He has published more than 330 papers in top journals, including Nature, Science, Cell, Nature Genetics, Proceedings of the national Academy of Science (PNAS), Annals of Statistics (AOS), Journal of the American Statistical Association (JASA), and Journal of the Royal Statistical Society (JRSSB), as well as presenting 55+ conference papers at top conferences including meetings for Neural Information Processing Systems (NeurIPS), Association for the Advancement of Artificial Intelligence (AAAI), Knowledge Discovery and Data Mining (KDD), International Conference on Data Mining (ICDM), Medical Image Computing and Computer Assisted Intervention (MICCAI) Society, and Intelligent Platform Management Interface (IPMI). He is the elected coordinating editor of JASA and the editor of JASA ACS.