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On October 5, 2020, the Medical Image Computing and Computer Assisted Intervention Society (MICCAI) 2020 conference hosted a virtual panel discussion with members of the Machine Learning Steering Subcommittee of the Radiological Society of North America. The MICCAI Society brings together scientists, engineers, physicians, educators, and students from around the world. Both societies share a vision to develop radiologic and medical imaging techniques through advanced quantitative imaging biomarkers and artificial intelligence. The panel elaborated on how collaborations between radiologists and machine learning scientists facilitate the creation and clinical success of imaging technology for radiology. This report presents structured highlights of the moderated dialogue at the panel. Keywords: Back-Propagation, Artificial Neural Network Algorithms, Machine Learning Algorithms © RSNA, 2021., Competing Interests: Disclosures of Conflicts of Interest: J.M. institution received grant from GE Healthcare; author receives royalties from GE Healthcare for licensing of pneumothorax detection algorithm; author paid for development of educational presentations from UCSF Postgraduate Medical Education; author’s spouse has been employed by AbbVie and Annexon Biosciences; author is associate editor of Radiology: Artificial Intelligence. J.K.C. institution received grants from GE, NIH, NSF, and Genentech; author received travel accommodations from IBM; author is deputy editor of Radiology: Artificial Intelligence. A.F. disclosed no relevant relationships. M.G.L. author is consultant to the National Institutes of Health for grant review; author is co-founder of PediaMetrix; author’s institution received grants from National Institutes of Health, National Science Foundation, Department of Defense, and Philips Healthcare; author has stock/stock options in PediaMetrix., (2021 by the Radiological Society of North America, Inc.)