Potential Applications of Machine Learning at Multidisciplinary Medical Team Meetings

TL;DR

This paper reviews the potential and challenges of applying machine learning to support multidisciplinary medical team meetings, based on a ten-year longitudinal study and ML method development.

Contribution

It provides a comprehensive analysis of MDT activities and explores opportunities and pitfalls for ML integration in complex medical team collaborations.

Findings

Longitudinal analysis of MDTs over ten years

Development of ML methods tailored for MDT support

Identification of key opportunities and challenges for ML in MDTs

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 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.