Maryellen Giger, Ph.D. is the A.N. Pritzker Distinguished Service Professor of Radiology, Committee on Medical Physics, and the College at the University of Chicago. She has been working, for decades, on computer-aided diagnosis/machine learning/deep learning in medical imaging for cancer, thoracic diseases, neuro-imaging, and other diseases diagnosis and management. Her AI research in cancer (breast cancer, thyroid cancer) for risk assessment, diagnosis, prognosis, and therapeutic response has yielded various translated components, and she has used these “virtual biopsies” in imaging-genomics association studies. She has extended her AI in medical imaging research to include the analysis of COVID-19 on CT and chest radiographs, and is contact PI on the NIBIB-funded Medical Imaging and Data Resource Center (MIDRC; midrc.org), which, as of February 2023, has ingested more than 300,000 medical imaging studies, with currently more than 135,000 imaging studies publicly available for use by AI investigators. Giger has more than 260 peer-reviewed publications and has more than 30 patents, and has mentored over 100 graduate students, residents, medical students, and undergraduate students. Giger is a former president of AAPM and of SPIE; a past member of the NIBIB Advisory Council of NIH; and is the Editor-in-Chief of the Journal of Medical Imaging. She is a member of the National Academy of Engineering (NAE), a recipient of the AAPM William D. Coolidge Gold Medal, a recipient of the SPIE Director’s Award, and the SPIE Harrison H. Barrett Award in Medical Imaging, a recipient of the RSNA’s Honored Educator Award and the RSNA’s Outstanding Researcher Award, and is a Fellow of AAPM, AIMBE, SPIE, SBMR, IEEE, IAMBE, and COS. In 2013, Giger was named by the International Congress on Medical Physics (ICMP) as one of the 50 medical physicists with the most impact on the field in the last 50 years. Giger was cofounder of Quantitative Insights [now Qlarity Imaging], which produces QuantX, the first FDA-cleared, machine-learning driven CADx (AI-aided) system.