Comment on "Black Box Prediction Methods in Sports Medicine Deserve a Red Card for Reckless Practice: A Change of Tactics is Needed to Advance Athlete Care"

TL;DR

This paper critiques claims against black-box machine learning models in sports medicine, emphasizing the importance of transparency and highlighting ongoing research that addresses these concerns to improve athlete injury prediction.

Contribution

It provides a nuanced discussion on the limitations and advancements in transparent machine learning methods for sports injury prediction, countering overly negative views.

Findings

Transparency is crucial for ML in sports medicine

Research is actively addressing black-box limitations

Ongoing developments improve injury risk prediction models

Abstract

In this paper we examine the claims made by Bullock et. al. on the applicability of black-box injury risk approaches in the sports injury domain. Overall, we agree that transparency is necessary for Machine Learning models to be useful in this field. However, there are areas of research that address precisely the concerns of the authors and strongly temper their conclusions. In the following we look at how these issues are being tackled by the Machine Learning community.