Machine Learning Prospects in Health 4.0 and Future Challenges

Abstract

The evolution of biomedical imaging and computer systems has allowed continued progress in computer-aided diagnostic (CAD) systems. The integration of machine learning in CAD systems has shown a significant advance in diagnostic accuracy in various tasks such as organ segmentation, pathology detection, and classification. However, there is still a long way to go in integrating machine learning with Industry 4.0 technologies such as cloud computing, big data, and the internet of things, so that it can be applied in health 4.0 (H4.0). In this research, machine learning techniques and their use in H4.0 will be discussed to promote the development of more personalized, predictive, and interconnected health services. Also, trends in the state-of-the-art and future directions will be shown.