Last words

Abstract

We ventured out to explore the role of machine learning (ML) in the medical technology industry and were rewarded with a fascinating tapestry that reveals the rich and layered nature of these applications. Indeed, even in this relatively short book, we saw ML applied to a large-scale problem - predicting the mortality rate of malaria as a result of policy changes in Chapter 2, to microscopic ISFET based neural networks used to analyse sweat contents directly on the skin surface in Chapter 10, while not forgetting the many exciting applications in between.It is undisputed that ML is a powerful technique that has been applied in many areas of the industry. In tandem, exciting research is on-going in many areas, which can provide novel and valuable tools for clinicians and improve the level of patient care. In spite of these benefits, the uptake of ML in the industry has been slow partly because of the heavily regulated nature of the medical devices industry as well as the fact that there is considerable hype around ML. Unfortunately, this is accompanied with unrealistic expectations and the subsequent disappointments have affected the standing of ML as a serious tool that can be used to improve health outcomes. Apart from inspiring readers about the possibilities of ML, we hope that this book can help with clarifying what ML means and correct any misconceptions about it.In this chapter, we will start by reviewing the preceding chapters of this book, beginning with the types of data that are used, followed by the ML frameworks and methods that are used. Clearly, effective implementation and deployment of ML is crucial for its success and there are a wide range of factors involved. We will focus solely on the types of platforms where ML is deployed and highlight some interesting methods that are used to overcome challenging conditions where ML is deployed. Since it is important for manufacturers to demonstrate regulatory compliance, we will discuss some of the issues that they face during the ML design and deployment phase. Finally, we will conclude the book with some thoughts about the future of ML in healthcare.