Machine learning technology is a rapidly growing field aiming to create
systems replicating human intelligence. In the healthcare sector, machine learning is
not meant to replace human physicians but to provide better solutions to healthcare
problems. It plays a critical role in the development of automated computational
approaches. It has numerous applications in radiology, computer-aided drug design,
virtual health assistance, clinical decisions, disease outbreaks, healthcare management,
and administration. Security and privacy risks are a significant concern with AIpowered healthcare systems since the healthcare sector has distinct security and privacy
requirements to safeguard patients' medical information. Despite this, using machine
learning in healthcare has many benefits, including faster analysis of large datasets,
improved safety of clinical trials, better insights into predictive screening, higher
accuracy, reduced healthcare costs, and increased efficiency. Although many AI and
machine learning applications have been successfully deployed in medical research and
continue to deliver favorable results, challenges still need to be addressed. In this book
chapter, we delve into the latest challenges and opportunities that the healthcare
industry faces. We explore the changing landscape of healthcare and provide insights
into how technological advancements, regulatory changes, and shifting patient
expectations are shaping the future of healthcare delivery. Whether you're a healthcare
professional, policymaker, or just interested in the industry, this chapter will provide
valuable insights and a fresh perspective on the challenges and opportunities faced by
the healthcare industry today.
Keywords: Artificial intelligence, Healthcare, Machine learning.
