Machine learning algorithms are increasingly finding applications in the healthcare sector. Whether assisting a clinician to process an individual patient's data or helping administrators view hospital bed turnover, the volume and complexity of healthcare data is a compelling reason for the development of machine learning based tools to aid in its interpretation and use.

• Chapter 1: Introduction
• Chapter 2: Health system planning and optimisation - advancements in the application of machine learning to policy decisions in global health
• Chapter 3: Health system preparedness - coordination and sharing of computation, models and data
• Chapter 4: Applications of machine learning for image-guided microsurgery
• Chapter 5: Electrophysiology and consciousness: a review
• Chapter 6: Brain networking and early diagnosis of Alzheimer's disease with machine learning
• Chapter 7: From classic machine learning to deep learning advances in atrial fibrillation detection
• Chapter 8: Dictionary learning techniques for left ventricle (LV) analysis and fibrosis detection in cardiac magnetic resonance imaging (MRI)
• Chapter 9: Enhancing physical performance with machine learning
• Chapter 10: Wearable electrochemical sensors and machine learning for real-time sweat analysis
• Chapter 11: Last words