Machine Learning in Healthcare: Why We Are Not Quite There Yet?
Machine learning promises to revolutionise medical applications such as diagnostics and clinimetrics. Recent progress in algorithms such as deep learning have pushed performance to human-level competence in some applications. However, these algorithms can give meaningless predictions for some kinds of data where humans would not. These confounded predictions could be perilous in mission-critical applications such as healthcare. I will argue that we will have to address difficult issues such as the nature of sampling and data collection from an imperfect world, the accountability of complex predictors, and the need for explanatory rather than just predictive power.
Prof. Max Little is an applied mathematician and statistician. He is a leading expert on clinical signal processing and machine learning algorithms for the use of consumer technologies such as telephones and smartphones to detect the symptoms of Parkinson's remotely. Along with being a Associate Professor of Mathematics at the University of Ashton, he is also a Senior Research Fellow at the University of Oxford and a Visiting Associate Professor at MIT.