Applications of Emerging Machine Learning Models in Healthcare Industry: A Comprehensive Review

Machine learning (ML) is crucial in modern healthcare because it can quickly and accurately detect hidden patterns that would otherwise go unnoticed. Current healthcare systems have only implemented a small number of the many ML applications that have been found or are currently being studied. Healthcare professionals face significant challenges when trying to implement ML applications due to dispersed data and a lack of well-structured, easily-explained documentation in the relevant sector, despite the fact that ML presents a huge potential in the healthcare system. In order to provide quick access to important information, this research sought to consolidate ML applications across healthcare domains in a more efficient and effective manner. Our research was organised into five main categories: community-based initiatives, healthcare operations management, early detection, remote care, risk management/preventive care, and healthcare operations. In order to facilitate easy access, we have divided these groupings into subgroups and included pertinent references and descriptions in tabular form. We want to drive healthcare professionals towards a healthcare system that is more based on machine learning, educate the public about the benefits of machine learning in healthcare, and close the knowledge gap among clinicians on the uses of machine learning.