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Computer-aided diagnosis of radiological images such as MRIs and X-rays was one of the first successful applications of AI to medicine. In this “human-in-the-loop” paradigm, computer vision models trained on large datasets of medical images detect potential signals of disease and make diagnoses, which are then manually checked by radiologists. This collaboration seeks to extend that loop by using computer models to help train future radiologists, via AI-driven tutorials that explain the patterns and predictions these algorithms have drawn from big data.

Using prostate MRIs as its initial focus, the researchers will develop an “AI teacher” that provides imaging examples alongside explanations of AI predictions. These tutorials will allow students to learn new AI-derived insights about image features to make diagnoses themselves, while familiarizing them with working in tandem with AI assistance. If successful, the tutorial concept could be transferable to training environments in pathology and other medical fields, as well as disciplines such as law and scientific research.


Chenhao Tan

Assistant Professor of Computer Science and Data Science

Yuxin Chen

Assistant Professor, Computer Science; Member: Committee on Computational and Applied Mathematics (CCAM)

Aytekin Oto

Professor of Radiology and Surgery, UChicago Medicine

Maryellen Giger

A.N. Pritzker Professor of Radiology, Committee on Medical Physics, and the College at the University of Chicago; AI+Science Research Initiative Advisory Board

Aritrick Chatterjee

Research Assistant Professor, UChicago