  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 longitudinal
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.
