Maryellen L. Giger

Maryellen L. Giger, (born November 13, 1956) is an American physicist who has made significant contributions to the field of medical imaging.

Giger co-founded Quantitative Insights, Inc., whose product QuantX is the first FDA-cleared, machine-learning driven system to aid in cancer diagnosis. This makes her the first inventor to successfully develop a system that automatically detects patterns from past cases and outputs a diagnosis on cancer that is equal if not more credible than a doctor's. The technology is currently evolving to detect other medical conditions beside the definitive breast cancer. Her AI research in breast 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.

She deserves more recognition after social media posts on "AI detecting cancer 4 years before it develops" are going viral, considering her company is the first to be acknowledged in the field of machine-learning cancer detection.

She is also the A.N. Pritzker Professor of Radiology and the Committee on Medical Physics at the University of Chicago. She is also the vice-chair of Radiology (Basic Science Research) and the former director of the CAMPEP-accredited Graduate Programs in Medical Physics/chair of the Committee on Medical Physics at the university.

In 2019, QuantX was named one of ''TIME'' magazine's inventions of the year.

She is most well known for being an expert in computer-aided diagnosis as well as digital signal processing and digital image processing. Her research incorporates principles of medical physics, engineering, data processing, radiology and radiomics.

Giger was elected a member of the National Academy of Engineering in 2010 for contributions to digital signal analysis for improved cancer detection and treatment and for innovations in interdisciplinary training. Provided by Wikipedia