Permission to make digital or hard copies of part or all of thi s work for personal or classroom use is granted without fee provided that copies ar e not made or distributed for profit or commercial advantage and that copies bear this n otice and the full citation on the first page. CSCW'19,, November 9th-13th 2019, Austin, T exas ACM 978-1-4503-6819-3/20/04. https://doi.org/10.1145/3334480.XXXXXXX Abstract While machine learning (ML) systems have produced great advances in several domains, their use in support of complex cooperative work remains a research challenge. A particularly challenging setting, and one that may benefit from ML support is the work of multidisciplinary medical teams (MDTs). This paper focuses on the activities performed during the multidisciplinary medical team meeting (MDTM), reviewing their main characteristics in light of a longitud inal analysis of several MDTs in a large teaching hospital over a period of ten years and of our development of ML methods to support MDTMs, and identifying opportunities and possible pitfalls for the use of ML to support MDTMs. Author Keywords Machine Learning; Speech and Language Processing; Mul-tidisciplinary Medical T eam Meeting; Collaboration Introduction An MDT is a group of specialists from different healthcare professions who collaborate on diagnosis and treatment of patients in their care.

We revisit a series of studies on the work of multidisciplinary medical teams with a view to identifying opportunities for the use of intelligent systems to support their complex cooperative work, and the challenges that might arise in developing such systems. We focus specially on the activities performed during the multidisciplinary medical team meeting (MDTM) and review the literature on MDTMs, as well as our own longitudinal analysis of several MDTs in a large teaching hospital over a period of ten years.