Machine learning (ML) is being used in many entities already and proven to be helpful. In many industries it is a great way
to increase the productivity. However “deep learning” (DL) has additional advantages in healthcare by mimetic process
and absorbs information. Artificial intelligence (AI) offers the chance to optimize routes for diagnosis and prognosis as
well as to generate individualized treatment plans. For instance, studies that include potential risk factors, such as un�derlying genetics and particular surroundings, may help with the creation of preventative measures and more precise
diagnosis using massive datasets which is used in ML. Additionally, the use of structural and functional imaging tools can
help healthcare professionals better understand present condition and plan and execute their treatment pathways.
Biomedical and healthcare sectors are increasingly applying “big data” for medical data analysis, which, in turn, is critical
for early disease diagnosis, treatment, and community healthcare. However, when the quality of the medical data is
lacking, the analysis’s accuracy suffers. For example, distinct regional diseases in different places have their own features,
which could make it harder to forecast when a disease would spread. In this chapter, we streamline ML techniques and how
effective is the DL techniques are and necessary to make accurate prediction in healthcare practices [1]. The neural
networkedriven DL mimics the human brain. It employs a multilayered neural network that generates results without the
need for preparing the input data. The algorithm receives the raw data from data scientists, evaluates it based on what it
already knows and what it can deduct from the new data, and then produces a decision. Contemporary issues need
contemporary solutions. There is a global shortage for healthcare professionals and ML was helpful with limitations. DL
could help to face some challenges. This chapter examines the modernization of healthcare industry through DL. It
provides an insight into what DL is and how it has revolutionized healthcare by proving beneficial in nearly every sector of
this mega-industry. From digitalized patient data to remote healthcare, DL has proven to be significant and advantageous to
patients and healthcare providers. Moreover, the chapter highlights the various opportunities, challenges, and threats,
which accompany integration of DL and “big data” into healthcare