Exploring the Potential of AI/ML Algorithms for Automated Diagnosis in Smart Healthcare

This paper explores the potential of artificial Intelligence/system gaining knowledge of (AI/ML) algorithms for automatic diagnosis in clever healthcare structures. Clever healthcare structures are those who rent technologies including remote tracking, information-driven selection aid, and natural language processing to screen and diagnose patients. Synthetic intelligence techniques consisting of gadget mastering algorithms are now gambling an increasingly important and critical function in such healthcare structures. Gadget studying techniques, such as supervised getting to know and reinforcement gaining knowledge of, can research from statistics and offer greater correct and efficient prognosis than conventional methods. On this paper, we overview special AI/ML algorithms and their utility to automatic prognosis in clever healthcare systems. We also discuss the challenges associated with AI/ML on every occasion its miles applied to healthcare structures. We recommend improvements to current AI/ML algorithms that would probably make them extra appropriate for automated diagnosis in smart healthcare systems. Eventually, we identify promising areas that require in addition studies. Basic, this paper gives a top level view of AI/ML for computerized analysis in smart healthcare and an important evaluation of the ability of system learning algorithms in this context.