Machine learning holds great potential for cancer diagnosis, prognosis, and prediction of response to therapy, but these promising deep-learning techniques have not yet become standard practice because of a lack of interpretability and transferability in translational medicine, according to Trey Ideker, PhD, University of California, San Diego. Dr. Ideker discussed approaches to overcome these barriers on Monday, April 10, during the session Interpreting and Building Trust in Artificial Intelligence Models, during the American Association for Cancer Research (AACR) Annual Meeting, April 8-13 in New Orleans. This AACR session and others can be viewed on the virtual platform by registered meeting participants through July 13, 2022. Registration can be done here. Over 19,000 scientists and physicians registered for this premier cancer conference, with ~80% (~15,200) attending in person and ~20% (~3,800) attending virtually. The AACR has over 50,000 members worldwide.
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