Interdisciplinary collaboration has become sought after by most institutions and corporations over the past few decades. This type of collaboration has grown exponentially since the advent of the internet and the information age. With the wave of interest to develop machine learning for the interpretation of diagnostic images it has become necessary for data scientists and radiologists to communicate through interdisciplinary research and collaboration. Such communication requires careful navigation for productive and meaningful outcomes. This article seeks to offer an overview of some previous literature discussing the best practices when forming interdisciplinary collaborative teams, explore some of the communication similarities and differences between the radiologist and data scientist disciplines, share some examples where pitfalls have caused confusion or frustration and re-work, and also to convey that, through trust, listening skills and knowing one's limitations, much can be learned and accomplished when working together.
Keywords: data scientists; domain expert; interdisciplinary; machine learning; radiology.
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