Machine Learning and Healthcare

1.1 Introduction

Machine learning (ML) is a subset of artificial intelligence (AI) that allows computer systems to learn and evolve based on experience without special programming. The applications of ML in healthcare are vast and have enormous potential to change the way we diagnose, treat and prevent disease. With the advent of electronic health records and the ability to collect and store vast amounts of health data, ML algorithms can learn patterns from this data to make real-time predictions and recommendations. The healthcare industry has been slow to adopt new technologies, but as the amount of data increases and computing power improves, ML has become an essential tool for healthcare providers. ML algorithms can help predict patient outcomes, identify high-risk patients, and customize treatment plans (Nayyar et al., 2021). ML can also be used to analyze medical images, detect diseases earlier and improve the accuracy of diagnoses. However, implementing ML in healthcare is not without its challenges. Data protection, security and algorithmic decision bias are concerns. In addition, healthcare providers may be resistant to change or lack the necessary skills to effectively use ML technology (Durga et al., 2019). Despite these challenges, the potential benefits of ML in healthcare are too great to ignore. As a techno-head I am interested in this field, I am excited to explore how ML can improve patient outcomes and improve healthcare. This chapter reviews the current state of ML in healthcare, its potential applications, and the challenges that must be addressed to realize its full potential.