The latest developments in Artificial Intelligence (AI) and Machine
Learning (ML) technology have led to significant progress in foreseeing and detecting
health crises, understanding disease prevalence, and analyzing disease states and
immune responses, to name a few applications. The growing abundance of electronic
health data represents a significant prospect within the healthcare field, offering the
potential for advancements in both research and practical healthcare enhancements.
Nevertheless, to effectively harness these data resources, healthcare epidemiologists
need computational methods capable of handling vast and intricate datasets. Over the
last ten years, the utilization of machine learning (ML) in the healthcare sector has
played a pivotal role in automating tasks for physicians, improving clinical capabilities,
and enhancing the availability of healthcare services. Machine learning (ML), which
focuses on developing tools and techniques for recognizing patterns in data, can be an
asset in this regard. This advancement underscores the critical importance of data at
every stage of ML, from model creation to its implementation. In this chapter, we offer
a perspective that centers around data, examining the innovations and obstacles that are
shaping the landscape of ML in healthcare.
Keywords: Artificial Intelligence, Clinical capabilities, Computational techniques, Healthcare, Machine learning.
