Automated Machine Learning (AutoML) has emerged as a promising approach to democratize machine learning techniques, enabling non-experts to build and deploy predictive models efficiently. In the healthcare domain, AutoML holds significant potential to accelerate the development of predictive models for various tasks, such as disease diagnosis, patient outcome prediction, and personalized treatment recommendation. This paper provides an overview of the current state of AutoML in healthcare, including its applications, challenges, and future directions. We discuss key considerations in applying AutoML to healthcare data, such as data privacy, interpretability, and model generalizability. Additionally, we highlight successful use cases of AutoML in healthcare and examine how it can complement traditional machine learning approaches. Finally, we explore potential avenues for future research and development in leveraging AutoML to improve healthcare outcomes.